{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Brendan\Desktop\Replication File_FPA\logfile.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 4 Feb 2021, 13:06:50

{com}. do "C:\Users\Brendan\Desktop\Replication File_FPA\Replication Do File.do"
{txt}
{com}. clear
{txt}
{com}. 
. use "C:\Users\Brendan\Desktop\Replication File_FPA\Main Data.dta" 
{txt}
{com}. 
. ********************************************************************************
. ***************************MAIN MANUSCRIPT**************************************
. ********************************************************************************
. 
. **TABLE 1
. //Model 1
. logit prosanctions loggedmigrants, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1147.3547}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1147.2751}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1147.2751}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      7.66
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0056
{txt}Log pseudolikelihood = {res}-1147.2751{txt}{col 49}Pseudo R2{col 67}= {res}    0.0213

{txt}{ralign 80:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}  prosanctions{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
loggedmigrants {c |}{col 16}{res}{space 2}-.1183671{col 28}{space 2} .0427592{col 39}{space 1}   -2.77{col 48}{space 3}0.006{col 56}{space 4}-.2021735{col 69}{space 3}-.0345607
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}  1.15057{col 28}{space 2} .3053176{col 39}{space 1}    3.77{col 48}{space 3}0.000{col 56}{space 4} .5521585{col 69}{space 3} 1.748981
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODEL1, title(Model 1)
{txt}
{com}. //Model 2
. logit prosanctions loggedmigrants libyabill iranbill, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-826.44131}  
Iteration 2:{space 3}log pseudolikelihood = {res:-824.71191}  
Iteration 3:{space 3}log pseudolikelihood = {res:-824.70603}  
Iteration 4:{space 3}log pseudolikelihood = {res:-824.70603}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}Wald chi2({res}3{txt}){col 67}= {res}    120.30
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-824.70603{txt}{col 49}Pseudo R2{col 67}= {res}    0.2965

{txt}{ralign 80:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}  prosanctions{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
loggedmigrants {c |}{col 16}{res}{space 2}-.0967547{col 28}{space 2} .0400949{col 39}{space 1}   -2.41{col 48}{space 3}0.016{col 56}{space 4}-.1753392{col 69}{space 3}-.0181702
{txt}{space 5}libyabill {c |}{col 16}{res}{space 2} 2.888441{col 28}{space 2} .2814598{col 39}{space 1}   10.26{col 48}{space 3}0.000{col 56}{space 4} 2.336789{col 69}{space 3} 3.440092
{txt}{space 6}iranbill {c |}{col 16}{res}{space 2}  3.25047{col 28}{space 2} .3425271{col 39}{space 1}    9.49{col 48}{space 3}0.000{col 56}{space 4} 2.579129{col 69}{space 3} 3.921811
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} -1.10335{col 28}{space 2}  .329135{col 39}{space 1}   -3.35{col 48}{space 3}0.001{col 56}{space 4}-1.748443{col 69}{space 3}-.4582576
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODEL2, title(Model 2)
{txt}
{com}. //Model 3
. logit prosanctions loggedmigrants libyabill iranbill i.ccode, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-794.86739}  
Iteration 2:{space 3}log pseudolikelihood = {res:-790.56996}  
Iteration 3:{space 3}log pseudolikelihood = {res:-790.54934}  
Iteration 4:{space 3}log pseudolikelihood = {res:-790.54934}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(2)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-790.54934{txt}{col 49}Pseudo R2{col 67}= {res}    0.3256

{txt}{ralign 80:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}  prosanctions{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
loggedmigrants {c |}{col 16}{res}{space 2}-.1187622{col 28}{space 2} .0856854{col 39}{space 1}   -1.39{col 48}{space 3}0.166{col 56}{space 4}-.2867024{col 69}{space 3} .0491781
{txt}{space 5}libyabill {c |}{col 16}{res}{space 2} 2.954435{col 28}{space 2}  .273097{col 39}{space 1}   10.82{col 48}{space 3}0.000{col 56}{space 4} 2.419175{col 69}{space 3} 3.489695
{txt}{space 6}iranbill {c |}{col 16}{res}{space 2} 3.365403{col 28}{space 2} .3716781{col 39}{space 1}    9.05{col 48}{space 3}0.000{col 56}{space 4} 2.636927{col 69}{space 3} 4.093879
{txt}{space 14} {c |}
{space 9}ccode {c |}
{space 10}205  {c |}{col 16}{res}{space 2} -1.80043{col 28}{space 2} .3129103{col 39}{space 1}   -5.75{col 48}{space 3}0.000{col 56}{space 4}-2.413723{col 69}{space 3}-1.187137
{txt}{space 10}210  {c |}{col 16}{res}{space 2}-.2517909{col 28}{space 2} .1361138{col 39}{space 1}   -1.85{col 48}{space 3}0.064{col 56}{space 4}-.5185691{col 69}{space 3} .0149873
{txt}{space 10}211  {c |}{col 16}{res}{space 2}-1.180178{col 28}{space 2}  .458198{col 39}{space 1}   -2.58{col 48}{space 3}0.010{col 56}{space 4} -2.07823{col 69}{space 3}-.2821264
{txt}{space 10}212  {c |}{col 16}{res}{space 2} -1.32523{col 28}{space 2} .7009801{col 39}{space 1}   -1.89{col 48}{space 3}0.059{col 56}{space 4}-2.699126{col 69}{space 3} .0486654
{txt}{space 10}220  {c |}{col 16}{res}{space 2}-1.521458{col 28}{space 2} .1289893{col 39}{space 1}  -11.80{col 48}{space 3}0.000{col 56}{space 4}-1.774272{col 69}{space 3}-1.268643
{txt}{space 10}230  {c |}{col 16}{res}{space 2}-1.283376{col 28}{space 2} .4597471{col 39}{space 1}   -2.79{col 48}{space 3}0.005{col 56}{space 4}-2.184464{col 69}{space 3}-.3822886
{txt}{space 10}235  {c |}{col 16}{res}{space 2}-.7981288{col 28}{space 2} .5351758{col 39}{space 1}   -1.49{col 48}{space 3}0.136{col 56}{space 4}-1.847054{col 69}{space 3} .2507965
{txt}{space 10}255  {c |}{col 16}{res}{space 2}-.6474255{col 28}{space 2} .0241371{col 39}{space 1}  -26.82{col 48}{space 3}0.000{col 56}{space 4}-.6947333{col 69}{space 3}-.6001177
{txt}{space 10}290  {c |}{col 16}{res}{space 2}-.3508963{col 28}{space 2} .3627568{col 39}{space 1}   -0.97{col 48}{space 3}0.333{col 56}{space 4}-1.061887{col 69}{space 3}  .360094
{txt}{space 10}305  {c |}{col 16}{res}{space 2}-1.155317{col 28}{space 2} .2142137{col 39}{space 1}   -5.39{col 48}{space 3}0.000{col 56}{space 4}-1.575169{col 69}{space 3}-.7354663
{txt}{space 10}310  {c |}{col 16}{res}{space 2}-.7861559{col 28}{space 2} .2884722{col 39}{space 1}   -2.73{col 48}{space 3}0.006{col 56}{space 4}-1.351551{col 69}{space 3}-.2207608
{txt}{space 10}316  {c |}{col 16}{res}{space 2}-.3057657{col 28}{space 2}  .373851{col 39}{space 1}   -0.82{col 48}{space 3}0.413{col 56}{space 4}  -1.0385{col 69}{space 3} .4269687
{txt}{space 10}317  {c |}{col 16}{res}{space 2}-.7538115{col 28}{space 2} .5010107{col 39}{space 1}   -1.50{col 48}{space 3}0.132{col 56}{space 4}-1.735775{col 69}{space 3} .2281515
{txt}{space 10}325  {c |}{col 16}{res}{space 2}-.8704633{col 28}{space 2} .0296033{col 39}{space 1}  -29.40{col 48}{space 3}0.000{col 56}{space 4}-.9284848{col 69}{space 3}-.8124419
{txt}{space 10}338  {c |}{col 16}{res}{space 2}-.2918777{col 28}{space 2} .3731587{col 39}{space 1}   -0.78{col 48}{space 3}0.434{col 56}{space 4}-1.023255{col 69}{space 3}    .4395
{txt}{space 10}344  {c |}{col 16}{res}{space 2}-.0499525{col 28}{space 2}  .864438{col 39}{space 1}   -0.06{col 48}{space 3}0.954{col 56}{space 4} -1.74422{col 69}{space 3} 1.644315
{txt}{space 10}349  {c |}{col 16}{res}{space 2}-.8101255{col 28}{space 2} .5467537{col 39}{space 1}   -1.48{col 48}{space 3}0.138{col 56}{space 4}-1.881743{col 69}{space 3}  .261492
{txt}{space 10}350  {c |}{col 16}{res}{space 2}-2.390697{col 28}{space 2}  .289911{col 39}{space 1}   -8.25{col 48}{space 3}0.000{col 56}{space 4}-2.958912{col 69}{space 3}-1.822482
{txt}{space 10}352  {c |}{col 16}{res}{space 2}-1.223995{col 28}{space 2} .3049726{col 39}{space 1}   -4.01{col 48}{space 3}0.000{col 56}{space 4} -1.82173{col 69}{space 3}-.6262599
{txt}{space 10}355  {c |}{col 16}{res}{space 2}-.6151084{col 28}{space 2} .3504061{col 39}{space 1}   -1.76{col 48}{space 3}0.079{col 56}{space 4}-1.301892{col 69}{space 3} .0716749
{txt}{space 10}360  {c |}{col 16}{res}{space 2}-.1987009{col 28}{space 2} .2774029{col 39}{space 1}   -0.72{col 48}{space 3}0.474{col 56}{space 4}-.7424007{col 69}{space 3} .3449988
{txt}{space 10}366  {c |}{col 16}{res}{space 2}-1.513446{col 28}{space 2} .8619502{col 39}{space 1}   -1.76{col 48}{space 3}0.079{col 56}{space 4}-3.202838{col 69}{space 3} .1759451
{txt}{space 10}367  {c |}{col 16}{res}{space 2}-1.258791{col 28}{space 2}  .631263{col 39}{space 1}   -1.99{col 48}{space 3}0.046{col 56}{space 4}-2.496044{col 69}{space 3}-.0215387
{txt}{space 10}368  {c |}{col 16}{res}{space 2}-.9807315{col 28}{space 2} .6807825{col 39}{space 1}   -1.44{col 48}{space 3}0.150{col 56}{space 4}-2.315041{col 69}{space 3} .3535777
{txt}{space 10}375  {c |}{col 16}{res}{space 2}-.0101445{col 28}{space 2} .3490944{col 39}{space 1}   -0.03{col 48}{space 3}0.977{col 56}{space 4} -.694357{col 69}{space 3}  .674068
{txt}{space 10}380  {c |}{col 16}{res}{space 2}-1.142269{col 28}{space 2}  .087468{col 39}{space 1}  -13.06{col 48}{space 3}0.000{col 56}{space 4}-1.313703{col 69}{space 3}-.9708347
{txt}{space 10}390  {c |}{col 16}{res}{space 2}-.7479704{col 28}{space 2} .2276655{col 39}{space 1}   -3.29{col 48}{space 3}0.001{col 56}{space 4}-1.194187{col 69}{space 3}-.3017542
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2}-.1691327{col 28}{space 2} .8876767{col 39}{space 1}   -0.19{col 48}{space 3}0.849{col 56}{space 4}-1.908947{col 69}{space 3} 1.570682
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODEL3, title(Model 3)
{txt}
{com}. //Model 4
. logit prosanctions loggedmigrants libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-640.84466}  
Iteration 2:{space 3}log pseudolikelihood = {res:-633.30402}  
Iteration 3:{space 3}log pseudolikelihood = {res:-632.92503}  
Iteration 4:{space 3}log pseudolikelihood = {res:-632.92081}  
Iteration 5:{space 3}log pseudolikelihood = {res:-632.92081}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(9)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-632.92081{txt}{col 49}Pseudo R2{col 67}= {res}    0.4601

{txt}{ralign 80:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}  prosanctions{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
loggedmigrants {c |}{col 16}{res}{space 2}-.1378095{col 28}{space 2} .0588368{col 39}{space 1}   -2.34{col 48}{space 3}0.019{col 56}{space 4}-.2531276{col 69}{space 3}-.0224914
{txt}{space 5}libyabill {c |}{col 16}{res}{space 2} 3.605257{col 28}{space 2} .3165535{col 39}{space 1}   11.39{col 48}{space 3}0.000{col 56}{space 4} 2.984824{col 69}{space 3} 4.225691
{txt}{space 6}iranbill {c |}{col 16}{res}{space 2} 3.926381{col 28}{space 2} .3953766{col 39}{space 1}    9.93{col 48}{space 3}0.000{col 56}{space 4} 3.151457{col 69}{space 3} 4.701305
{txt}{space 14} {c |}
{space 9}ccode {c |}
{space 10}205  {c |}{col 16}{res}{space 2}-1.315565{col 28}{space 2} .2463079{col 39}{space 1}   -5.34{col 48}{space 3}0.000{col 56}{space 4} -1.79832{col 69}{space 3}-.8328103
{txt}{space 10}210  {c |}{col 16}{res}{space 2} .7457135{col 28}{space 2} .2075794{col 39}{space 1}    3.59{col 48}{space 3}0.000{col 56}{space 4} .3388654{col 69}{space 3} 1.152562
{txt}{space 10}211  {c |}{col 16}{res}{space 2}-1.077377{col 28}{space 2} .3004121{col 39}{space 1}   -3.59{col 48}{space 3}0.000{col 56}{space 4}-1.666174{col 69}{space 3}-.4885803
{txt}{space 10}212  {c |}{col 16}{res}{space 2}-1.128633{col 28}{space 2} .4861851{col 39}{space 1}   -2.32{col 48}{space 3}0.020{col 56}{space 4}-2.081539{col 69}{space 3}-.1757283
{txt}{space 10}220  {c |}{col 16}{res}{space 2}-.8602942{col 28}{space 2} .2009167{col 39}{space 1}   -4.28{col 48}{space 3}0.000{col 56}{space 4}-1.254084{col 69}{space 3}-.4665047
{txt}{space 10}230  {c |}{col 16}{res}{space 2}-.9835756{col 28}{space 2}  .345286{col 39}{space 1}   -2.85{col 48}{space 3}0.004{col 56}{space 4}-1.660324{col 69}{space 3}-.3068276
{txt}{space 10}235  {c |}{col 16}{res}{space 2}-.4820532{col 28}{space 2} .4108319{col 39}{space 1}   -1.17{col 48}{space 3}0.241{col 56}{space 4}-1.287269{col 69}{space 3} .3231625
{txt}{space 10}255  {c |}{col 16}{res}{space 2}-.0817069{col 28}{space 2} .0958741{col 39}{space 1}   -0.85{col 48}{space 3}0.394{col 56}{space 4}-.2696166{col 69}{space 3} .1062029
{txt}{space 10}290  {c |}{col 16}{res}{space 2}-.6815966{col 28}{space 2} .3003638{col 39}{space 1}   -2.27{col 48}{space 3}0.023{col 56}{space 4}-1.270299{col 69}{space 3}-.0928944
{txt}{space 10}305  {c |}{col 16}{res}{space 2} -.493214{col 28}{space 2} .3122449{col 39}{space 1}   -1.58{col 48}{space 3}0.114{col 56}{space 4}-1.105203{col 69}{space 3} .1187748
{txt}{space 10}310  {c |}{col 16}{res}{space 2}-.6613846{col 28}{space 2} .2425386{col 39}{space 1}   -2.73{col 48}{space 3}0.006{col 56}{space 4}-1.136751{col 69}{space 3}-.1860177
{txt}{space 10}316  {c |}{col 16}{res}{space 2}-.1322699{col 28}{space 2} .3202442{col 39}{space 1}   -0.41{col 48}{space 3}0.680{col 56}{space 4}-.7599369{col 69}{space 3} .4953972
{txt}{space 10}317  {c |}{col 16}{res}{space 2}-1.086068{col 28}{space 2} .3710223{col 39}{space 1}   -2.93{col 48}{space 3}0.003{col 56}{space 4}-1.813258{col 69}{space 3}-.3588771
{txt}{space 10}325  {c |}{col 16}{res}{space 2}-.6173701{col 28}{space 2} .1390566{col 39}{space 1}   -4.44{col 48}{space 3}0.000{col 56}{space 4}-.8899161{col 69}{space 3} -.344824
{txt}{space 10}338  {c |}{col 16}{res}{space 2}-.5925894{col 28}{space 2} .2573762{col 39}{space 1}   -2.30{col 48}{space 3}0.021{col 56}{space 4}-1.097038{col 69}{space 3}-.0881412
{txt}{space 10}344  {c |}{col 16}{res}{space 2}-.2799893{col 28}{space 2} .5843872{col 39}{space 1}   -0.48{col 48}{space 3}0.632{col 56}{space 4}-1.425367{col 69}{space 3} .8653886
{txt}{space 10}349  {c |}{col 16}{res}{space 2}-.9822972{col 28}{space 2} .3686441{col 39}{space 1}   -2.66{col 48}{space 3}0.008{col 56}{space 4}-1.704826{col 69}{space 3}-.2597681
{txt}{space 10}350  {c |}{col 16}{res}{space 2}-1.572971{col 28}{space 2}  .245407{col 39}{space 1}   -6.41{col 48}{space 3}0.000{col 56}{space 4} -2.05396{col 69}{space 3}-1.091982
{txt}{space 10}352  {c |}{col 16}{res}{space 2}-.4500597{col 28}{space 2} .3178195{col 39}{space 1}   -1.42{col 48}{space 3}0.157{col 56}{space 4}-1.072974{col 69}{space 3}  .172855
{txt}{space 10}355  {c |}{col 16}{res}{space 2}-.9272651{col 28}{space 2}  .242147{col 39}{space 1}   -3.83{col 48}{space 3}0.000{col 56}{space 4}-1.401865{col 69}{space 3}-.4526656
{txt}{space 10}360  {c |}{col 16}{res}{space 2}-.4055224{col 28}{space 2} .1740712{col 39}{space 1}   -2.33{col 48}{space 3}0.020{col 56}{space 4}-.7466956{col 69}{space 3}-.0643492
{txt}{space 10}366  {c |}{col 16}{res}{space 2}-1.658827{col 28}{space 2} .5628448{col 39}{space 1}   -2.95{col 48}{space 3}0.003{col 56}{space 4}-2.761982{col 69}{space 3}-.5556712
{txt}{space 10}367  {c |}{col 16}{res}{space 2}-.9112233{col 28}{space 2} .4661624{col 39}{space 1}   -1.95{col 48}{space 3}0.051{col 56}{space 4}-1.824885{col 69}{space 3} .0024382
{txt}{space 10}368  {c |}{col 16}{res}{space 2}-1.084109{col 28}{space 2} .5106011{col 39}{space 1}   -2.12{col 48}{space 3}0.034{col 56}{space 4}-2.084869{col 69}{space 3}-.0833496
{txt}{space 10}375  {c |}{col 16}{res}{space 2} .4552819{col 28}{space 2} .2630964{col 39}{space 1}    1.73{col 48}{space 3}0.084{col 56}{space 4}-.0603776{col 69}{space 3} .9709414
{txt}{space 10}380  {c |}{col 16}{res}{space 2}-.6358924{col 28}{space 2} .0981771{col 39}{space 1}   -6.48{col 48}{space 3}0.000{col 56}{space 4}-.8283159{col 69}{space 3}-.4434689
{txt}{space 10}390  {c |}{col 16}{res}{space 2}-.3710878{col 28}{space 2} .1785623{col 39}{space 1}   -2.08{col 48}{space 3}0.038{col 56}{space 4}-.7210635{col 69}{space 3} -.021112
{txt}{space 14} {c |}
{space 9}party {c |}
{space 10}EPP  {c |}{col 16}{res}{space 2} 2.256738{col 28}{space 2} .9622463{col 39}{space 1}    2.35{col 48}{space 3}0.019{col 56}{space 4} .3707699{col 69}{space 3} 4.142706
{txt}{space 10}S&D  {c |}{col 16}{res}{space 2}  2.24755{col 28}{space 2} .9772144{col 39}{space 1}    2.30{col 48}{space 3}0.021{col 56}{space 4} .3322447{col 69}{space 3} 4.162855
{txt}{space 3}Greens/EFA  {c |}{col 16}{res}{space 2}-.7322755{col 28}{space 2} .9645771{col 39}{space 1}   -0.76{col 48}{space 3}0.448{col 56}{space 4}-2.622812{col 69}{space 3} 1.158261
{txt}{space 4}ALDE/ADLE  {c |}{col 16}{res}{space 2} 2.353044{col 28}{space 2} 1.008389{col 39}{space 1}    2.33{col 48}{space 3}0.020{col 56}{space 4} .3766377{col 69}{space 3}  4.32945
{txt}{space 10}ECR  {c |}{col 16}{res}{space 2} 4.148581{col 28}{space 2} 1.015399{col 39}{space 1}    4.09{col 48}{space 3}0.000{col 56}{space 4} 2.158435{col 69}{space 3} 6.138727
{txt}{space 9}EFDD  {c |}{col 16}{res}{space 2} .2443284{col 28}{space 2} .9543523{col 39}{space 1}    0.26{col 48}{space 3}0.798{col 56}{space 4}-1.626168{col 69}{space 3} 2.114824
{txt}{space 6}GUE-NGL  {c |}{col 16}{res}{space 2}-.9679572{col 28}{space 2} 1.258304{col 39}{space 1}   -0.77{col 48}{space 3}0.442{col 56}{space 4}-3.434187{col 69}{space 3} 1.498273
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2}-2.343782{col 28}{space 2} 1.200916{col 39}{space 1}   -1.95{col 48}{space 3}0.051{col 56}{space 4}-4.697535{col 69}{space 3} .0099703
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODEL4, title(Model 4)
{txt}
{com}. //Model 5
. logit prosanctions loggedmigrants population unemployment gdpgrowth distance col libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-635.53068}  
Iteration 2:{space 3}log pseudolikelihood = {res:-627.45257}  
Iteration 3:{space 3}log pseudolikelihood = {res: -627.0376}  
Iteration 4:{space 3}log pseudolikelihood = {res:-627.03436}  
Iteration 5:{space 3}log pseudolikelihood = {res:-627.03436}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(14)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-627.03436{txt}{col 49}Pseudo R2{col 67}= {res}    0.4651

{txt}{ralign 80:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}  prosanctions{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
loggedmigrants {c |}{col 16}{res}{space 2}-.2281655{col 28}{space 2}   .08724{col 39}{space 1}   -2.62{col 48}{space 3}0.009{col 56}{space 4}-.3991528{col 69}{space 3}-.0571782
{txt}{space 4}population {c |}{col 16}{res}{space 2} .3583438{col 28}{space 2} .2371057{col 39}{space 1}    1.51{col 48}{space 3}0.131{col 56}{space 4}-.1063747{col 69}{space 3} .8230624
{txt}{space 2}unemployment {c |}{col 16}{res}{space 2}-.0998719{col 28}{space 2} .0583547{col 39}{space 1}   -1.71{col 48}{space 3}0.087{col 56}{space 4} -.214245{col 69}{space 3} .0145012
{txt}{space 5}gdpgrowth {c |}{col 16}{res}{space 2} .0962875{col 28}{space 2} .1098631{col 39}{space 1}    0.88{col 48}{space 3}0.381{col 56}{space 4}-.1190402{col 69}{space 3} .3116151
{txt}{space 6}distance {c |}{col 16}{res}{space 2} .0005691{col 28}{space 2}  .000285{col 39}{space 1}    2.00{col 48}{space 3}0.046{col 56}{space 4} .0000104{col 69}{space 3} .0011278
{txt}{space 11}col {c |}{col 16}{res}{space 2} .8387646{col 28}{space 2} .3106687{col 39}{space 1}    2.70{col 48}{space 3}0.007{col 56}{space 4} .2298651{col 69}{space 3} 1.447664
{txt}{space 5}libyabill {c |}{col 16}{res}{space 2} 3.831456{col 28}{space 2} .3439807{col 39}{space 1}   11.14{col 48}{space 3}0.000{col 56}{space 4} 3.157266{col 69}{space 3} 4.505646
{txt}{space 6}iranbill {c |}{col 16}{res}{space 2} 3.744422{col 28}{space 2}  .376194{col 39}{space 1}    9.95{col 48}{space 3}0.000{col 56}{space 4} 3.007096{col 69}{space 3} 4.481749
{txt}{space 14} {c |}
{space 9}ccode {c |}
{space 10}205  {c |}{col 16}{res}{space 2} 19.85469{col 28}{space 2} 13.97587{col 39}{space 1}    1.42{col 48}{space 3}0.155{col 56}{space 4}-7.537504{col 69}{space 3} 47.24689
{txt}{space 10}210  {c |}{col 16}{res}{space 2} 17.74489{col 28}{space 2} 11.01035{col 39}{space 1}    1.61{col 48}{space 3}0.107{col 56}{space 4} -3.83499{col 69}{space 3} 39.32477
{txt}{space 10}211  {c |}{col 16}{res}{space 2} 17.76773{col 28}{space 2} 12.10695{col 39}{space 1}    1.47{col 48}{space 3}0.142{col 56}{space 4}-5.961456{col 69}{space 3} 41.49692
{txt}{space 10}212  {c |}{col 16}{res}{space 2} 20.68943{col 28}{space 2} 14.58168{col 39}{space 1}    1.42{col 48}{space 3}0.156{col 56}{space 4}-7.890134{col 69}{space 3} 49.26899
{txt}{space 10}220  {c |}{col 16}{res}{space 2}-1.173616{col 28}{space 2} .5713035{col 39}{space 1}   -2.05{col 48}{space 3}0.040{col 56}{space 4} -2.29335{col 69}{space 3} -.053882
{txt}{space 10}230  {c |}{col 16}{res}{space 2} 6.771872{col 28}{space 2} 4.278919{col 39}{space 1}    1.58{col 48}{space 3}0.114{col 56}{space 4}-1.614655{col 69}{space 3}  15.1584
{txt}{space 10}235  {c |}{col 16}{res}{space 2} 18.64486{col 28}{space 2} 12.26001{col 39}{space 1}    1.52{col 48}{space 3}0.128{col 56}{space 4}-5.384316{col 69}{space 3} 42.67404
{txt}{space 10}255  {c |}{col 16}{res}{space 2}-6.045986{col 28}{space 2} 4.098657{col 39}{space 1}   -1.48{col 48}{space 3}0.140{col 56}{space 4}-14.07921{col 69}{space 3} 1.987235
{txt}{space 10}290  {c |}{col 16}{res}{space 2} 8.901077{col 28}{space 2} 6.092125{col 39}{space 1}    1.46{col 48}{space 3}0.144{col 56}{space 4}-3.039269{col 69}{space 3} 20.84142
{txt}{space 10}305  {c |}{col 16}{res}{space 2} 19.70185{col 28}{space 2} 12.98973{col 39}{space 1}    1.52{col 48}{space 3}0.129{col 56}{space 4}-5.757559{col 69}{space 3} 45.16126
{txt}{space 10}310  {c |}{col 16}{res}{space 2}  19.3605{col 28}{space 2} 12.86073{col 39}{space 1}    1.51{col 48}{space 3}0.132{col 56}{space 4}-5.846071{col 69}{space 3} 44.56707
{txt}{space 10}316  {c |}{col 16}{res}{space 2} 19.21734{col 28}{space 2} 12.37184{col 39}{space 1}    1.55{col 48}{space 3}0.120{col 56}{space 4}-5.031021{col 69}{space 3}  43.4657
{txt}{space 10}317  {c |}{col 16}{res}{space 2} 20.64091{col 28}{space 2} 13.92346{col 39}{space 1}    1.48{col 48}{space 3}0.138{col 56}{space 4}-6.648565{col 69}{space 3} 47.93038
{txt}{space 10}325  {c |}{col 16}{res}{space 2} 1.721147{col 28}{space 2} 1.026506{col 39}{space 1}    1.68{col 48}{space 3}0.094{col 56}{space 4}-.2907673{col 69}{space 3} 3.733061
{txt}{space 10}338  {c |}{col 16}{res}{space 2}  22.6682{col 28}{space 2} 15.09781{col 39}{space 1}    1.50{col 48}{space 3}0.133{col 56}{space 4}-6.922973{col 69}{space 3} 52.25937
{txt}{space 10}344  {c |}{col 16}{res}{space 2} 22.29149{col 28}{space 2} 13.84198{col 39}{space 1}    1.61{col 48}{space 3}0.107{col 56}{space 4}-4.838288{col 69}{space 3} 49.42127
{txt}{space 10}349  {c |}{col 16}{res}{space 2} 21.56275{col 28}{space 2} 14.36208{col 39}{space 1}    1.50{col 48}{space 3}0.133{col 56}{space 4}-6.586399{col 69}{space 3}  49.7119
{txt}{space 10}350  {c |}{col 16}{res}{space 2} 20.24999{col 28}{space 2} 12.92582{col 39}{space 1}    1.57{col 48}{space 3}0.117{col 56}{space 4} -5.08415{col 69}{space 3} 45.58414
{txt}{space 10}352  {c |}{col 16}{res}{space 2} 24.01404{col 28}{space 2} 14.86952{col 39}{space 1}    1.61{col 48}{space 3}0.106{col 56}{space 4}-5.129691{col 69}{space 3} 53.15776
{txt}{space 10}355  {c |}{col 16}{res}{space 2} 20.54922{col 28}{space 2} 13.44023{col 39}{space 1}    1.53{col 48}{space 3}0.126{col 56}{space 4}-5.793143{col 69}{space 3} 46.89159
{txt}{space 10}360  {c |}{col 16}{res}{space 2} 16.14205{col 28}{space 2} 10.30613{col 39}{space 1}    1.57{col 48}{space 3}0.117{col 56}{space 4}-4.057586{col 69}{space 3} 36.34169
{txt}{space 10}366  {c |}{col 16}{res}{space 2} 20.43306{col 28}{space 2} 14.25068{col 39}{space 1}    1.43{col 48}{space 3}0.152{col 56}{space 4}-7.497759{col 69}{space 3} 48.36388
{txt}{space 10}367  {c |}{col 16}{res}{space 2} 21.60576{col 28}{space 2} 14.42068{col 39}{space 1}    1.50{col 48}{space 3}0.134{col 56}{space 4}-6.658254{col 69}{space 3} 49.86977
{txt}{space 10}368  {c |}{col 16}{res}{space 2} 21.11833{col 28}{space 2} 14.28717{col 39}{space 1}    1.48{col 48}{space 3}0.139{col 56}{space 4}-6.884005{col 69}{space 3} 49.12067
{txt}{space 10}375  {c |}{col 16}{res}{space 2} 21.47683{col 28}{space 2} 13.65535{col 39}{space 1}    1.57{col 48}{space 3}0.116{col 56}{space 4}-5.287167{col 69}{space 3} 48.24082
{txt}{space 10}380  {c |}{col 16}{res}{space 2} 18.97198{col 28}{space 2} 12.88755{col 39}{space 1}    1.47{col 48}{space 3}0.141{col 56}{space 4}-6.287153{col 69}{space 3} 44.23112
{txt}{space 10}390  {c |}{col 16}{res}{space 2} 20.63594{col 28}{space 2} 13.56436{col 39}{space 1}    1.52{col 48}{space 3}0.128{col 56}{space 4}-5.949713{col 69}{space 3} 47.22159
{txt}{space 14} {c |}
{space 9}party {c |}
{space 10}EPP  {c |}{col 16}{res}{space 2} 2.186067{col 28}{space 2} .9377774{col 39}{space 1}    2.33{col 48}{space 3}0.020{col 56}{space 4} .3480572{col 69}{space 3} 4.024077
{txt}{space 10}S&D  {c |}{col 16}{res}{space 2} 2.182074{col 28}{space 2} .9535556{col 39}{space 1}    2.29{col 48}{space 3}0.022{col 56}{space 4}  .313139{col 69}{space 3} 4.051008
{txt}{space 3}Greens/EFA  {c |}{col 16}{res}{space 2}-.8056865{col 28}{space 2} .9405854{col 39}{space 1}   -0.86{col 48}{space 3}0.392{col 56}{space 4}  -2.6492{col 69}{space 3} 1.037827
{txt}{space 4}ALDE/ADLE  {c |}{col 16}{res}{space 2} 2.329472{col 28}{space 2} .9917737{col 39}{space 1}    2.35{col 48}{space 3}0.019{col 56}{space 4} .3856307{col 69}{space 3} 4.273312
{txt}{space 10}ECR  {c |}{col 16}{res}{space 2} 3.993733{col 28}{space 2} .9879399{col 39}{space 1}    4.04{col 48}{space 3}0.000{col 56}{space 4} 2.057406{col 69}{space 3} 5.930059
{txt}{space 9}EFDD  {c |}{col 16}{res}{space 2} .0919102{col 28}{space 2} .9021175{col 39}{space 1}    0.10{col 48}{space 3}0.919{col 56}{space 4}-1.676208{col 69}{space 3} 1.860028
{txt}{space 6}GUE-NGL  {c |}{col 16}{res}{space 2}-.9796773{col 28}{space 2} 1.213121{col 39}{space 1}   -0.81{col 48}{space 3}0.419{col 56}{space 4} -3.35735{col 69}{space 3} 1.397995
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2}-25.80527{col 28}{space 2} 14.81133{col 39}{space 1}   -1.74{col 48}{space 3}0.081{col 56}{space 4}-54.83495{col 69}{space 3} 3.224407
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODEL5, title(Model 5)
{txt}
{com}. 
. estout MODEL1 MODEL2 MODEL3 MODEL4 MODEL5, cells(b(star fmt(3)) se(par fmt(3))) starlevels($^{c -(}+{c )-}$ 0.10 $^{c -(}*{c )-}$ 0.05 $^{c -(}**{c )-}$ 0.01 $^{c -(}***{c )-}$ 0.001) stats(N N_g r2, fmt(0 0 3) label(Observations Countries R$^2$)) label,  using "results1.tex", replace style(tex)
{res}{txt}(note: file results1.tex not found)
(output written to {browse  `"results1.tex"'})

{com}. 
. **TABLE 2
. //Model 6
. logit prosanctions ln_transitstock_weighted if libyabill==1, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -348.4857}  
Iteration 1:{space 3}log pseudolikelihood = {res:-336.01165}  
Iteration 2:{space 3}log pseudolikelihood = {res: -335.7466}  
Iteration 3:{space 3}log pseudolikelihood = {res:-335.74646}  
Iteration 4:{space 3}log pseudolikelihood = {res:-335.74646}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       640
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      8.27
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0040
{txt}Log pseudolikelihood = {res}-335.74646{txt}{col 49}Pseudo R2{col 67}= {res}    0.0366

{txt}{ralign 90:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            prosanctions{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      z{col 58}   P>|z|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_transitstock_weighted {c |}{col 26}{res}{space 2}-.2002875{col 38}{space 2} .0696446{col 49}{space 1}   -2.88{col 58}{space 3}0.004{col 66}{space 4}-.3367885{col 79}{space 3}-.0637866
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} 3.418314{col 38}{space 2} .7217906{col 49}{space 1}    4.74{col 58}{space 3}0.000{col 66}{space 4} 2.003631{col 79}{space 3} 4.832998
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODEL6, title(Model 6)
{txt}
{com}. //Model 7
. logit prosanctions ln_transitstock_weighted population unemployment gdpgrowth distance col if libyabill==1, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -348.4857}  
Iteration 1:{space 3}log pseudolikelihood = {res:-323.67453}  
Iteration 2:{space 3}log pseudolikelihood = {res:-321.90671}  
Iteration 3:{space 3}log pseudolikelihood = {res:-321.89951}  
Iteration 4:{space 3}log pseudolikelihood = {res:-321.89951}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       640
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(5)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-321.89951{txt}{col 49}Pseudo R2{col 67}= {res}    0.0763

{txt}{ralign 90:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            prosanctions{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      z{col 58}   P>|z|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_transitstock_weighted {c |}{col 26}{res}{space 2}-.3715509{col 38}{space 2} .0954248{col 49}{space 1}   -3.89{col 58}{space 3}0.000{col 66}{space 4}-.5585801{col 79}{space 3}-.1845217
{txt}{space 14}population {c |}{col 26}{res}{space 2}  .016885{col 38}{space 2} .0073365{col 49}{space 1}    2.30{col 58}{space 3}0.021{col 66}{space 4} .0025057{col 79}{space 3} .0312644
{txt}{space 12}unemployment {c |}{col 26}{res}{space 2}-.0414754{col 38}{space 2} .0304137{col 49}{space 1}   -1.36{col 58}{space 3}0.173{col 66}{space 4}-.1010852{col 79}{space 3} .0181344
{txt}{space 15}gdpgrowth {c |}{col 26}{res}{space 2} .0868877{col 38}{space 2} .1538921{col 49}{space 1}    0.56{col 58}{space 3}0.572{col 66}{space 4}-.2147353{col 79}{space 3} .3885107
{txt}{space 16}distance {c |}{col 26}{res}{space 2} .0003601{col 38}{space 2} .0003856{col 49}{space 1}    0.93{col 58}{space 3}0.350{col 66}{space 4}-.0003957{col 79}{space 3}  .001116
{txt}{space 21}col {c |}{col 26}{res}{space 2} .1591593{col 38}{space 2} .5666678{col 49}{space 1}    0.28{col 58}{space 3}0.779{col 66}{space 4}-.9514892{col 79}{space 3} 1.269808
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} 4.223492{col 38}{space 2} 1.250535{col 49}{space 1}    3.38{col 58}{space 3}0.001{col 66}{space 4} 1.772488{col 79}{space 3} 6.674496
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODEL7, title(Model 7)
{txt}
{com}. //Model 8
. logit prosanctions migrants_plustransit_sd libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-640.71255}  
Iteration 2:{space 3}log pseudolikelihood = {res:-633.18628}  
Iteration 3:{space 3}log pseudolikelihood = {res:-632.79354}  
Iteration 4:{space 3}log pseudolikelihood = {res:-632.78888}  
Iteration 5:{space 3}log pseudolikelihood = {res:-632.78888}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(9)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-632.78888{txt}{col 49}Pseudo R2{col 67}= {res}    0.4602

{txt}{ralign 89:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}           prosanctions{col 25}{c |}      Coef.{col 37}   Std. Err.{col 49}      z{col 57}   P>|z|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
migrants_plustransit_sd {c |}{col 25}{res}{space 2}-.2478211{col 37}{space 2} .0997579{col 48}{space 1}   -2.48{col 57}{space 3}0.013{col 65}{space 4}-.4433429{col 78}{space 3}-.0522993
{txt}{space 14}libyabill {c |}{col 25}{res}{space 2} 4.127481{col 37}{space 2} .3627439{col 48}{space 1}   11.38{col 57}{space 3}0.000{col 65}{space 4} 3.416516{col 78}{space 3} 4.838446
{txt}{space 15}iranbill {c |}{col 25}{res}{space 2} 3.994513{col 37}{space 2} .3944076{col 48}{space 1}   10.13{col 57}{space 3}0.000{col 65}{space 4} 3.221488{col 78}{space 3} 4.767538
{txt}{space 23} {c |}
{space 18}ccode {c |}
{space 19}205  {c |}{col 25}{res}{space 2}-1.512406{col 37}{space 2} .3027577{col 48}{space 1}   -5.00{col 57}{space 3}0.000{col 65}{space 4}  -2.1058{col 78}{space 3}-.9190118
{txt}{space 19}210  {c |}{col 25}{res}{space 2} .4311997{col 37}{space 2} .2609675{col 48}{space 1}    1.65{col 57}{space 3}0.098{col 65}{space 4}-.0802873{col 78}{space 3} .9426867
{txt}{space 19}211  {c |}{col 25}{res}{space 2}-1.020595{col 37}{space 2} .2922434{col 48}{space 1}   -3.49{col 57}{space 3}0.000{col 65}{space 4}-1.593381{col 78}{space 3}-.4478081
{txt}{space 19}212  {c |}{col 25}{res}{space 2}-.6196533{col 37}{space 2} .2786044{col 48}{space 1}   -2.22{col 57}{space 3}0.026{col 65}{space 4}-1.165708{col 78}{space 3}-.0735987
{txt}{space 19}220  {c |}{col 25}{res}{space 2}-1.018286{col 37}{space 2} .2049043{col 48}{space 1}   -4.97{col 57}{space 3}0.000{col 65}{space 4}-1.419892{col 78}{space 3}-.6166814
{txt}{space 19}230  {c |}{col 25}{res}{space 2}-.8674062{col 37}{space 2} .2610161{col 48}{space 1}   -3.32{col 57}{space 3}0.001{col 65}{space 4}-1.378988{col 78}{space 3}-.3558241
{txt}{space 19}235  {c |}{col 25}{res}{space 2}-.2519727{col 37}{space 2} .2854694{col 48}{space 1}   -0.88{col 57}{space 3}0.377{col 65}{space 4}-.8114824{col 78}{space 3} .3075371
{txt}{space 19}255  {c |}{col 25}{res}{space 2}-.0948448{col 37}{space 2} .0914702{col 48}{space 1}   -1.04{col 57}{space 3}0.300{col 65}{space 4}-.2741231{col 78}{space 3} .0844336
{txt}{space 19}290  {c |}{col 25}{res}{space 2}-.7511196{col 37}{space 2}   .28365{col 48}{space 1}   -2.65{col 57}{space 3}0.008{col 65}{space 4}-1.307064{col 78}{space 3}-.1951758
{txt}{space 19}305  {c |}{col 25}{res}{space 2}-.8012432{col 37}{space 2} .3624163{col 48}{space 1}   -2.21{col 57}{space 3}0.027{col 65}{space 4}-1.511566{col 78}{space 3}-.0909202
{txt}{space 19}310  {c |}{col 25}{res}{space 2} -.883909{col 37}{space 2} .2885881{col 48}{space 1}   -3.06{col 57}{space 3}0.002{col 65}{space 4}-1.449531{col 78}{space 3}-.3182867
{txt}{space 19}316  {c |}{col 25}{res}{space 2}-.2240626{col 37}{space 2} .3041372{col 48}{space 1}   -0.74{col 57}{space 3}0.461{col 65}{space 4}-.8201606{col 78}{space 3} .3720354
{txt}{space 19}317  {c |}{col 25}{res}{space 2}-.9006243{col 37}{space 2} .2790074{col 48}{space 1}   -3.23{col 57}{space 3}0.001{col 65}{space 4}-1.447469{col 78}{space 3}-.3537798
{txt}{space 19}325  {c |}{col 25}{res}{space 2} -.874387{col 37}{space 2} .1881877{col 48}{space 1}   -4.65{col 57}{space 3}0.000{col 65}{space 4}-1.243228{col 78}{space 3}-.5055458
{txt}{space 19}338  {c |}{col 25}{res}{space 2}-.6152607{col 37}{space 2} .2608796{col 48}{space 1}   -2.36{col 57}{space 3}0.018{col 65}{space 4}-1.126575{col 78}{space 3}-.1039461
{txt}{space 19}344  {c |}{col 25}{res}{space 2} .2684939{col 37}{space 2} .3563312{col 48}{space 1}    0.75{col 57}{space 3}0.451{col 65}{space 4}-.4299024{col 78}{space 3} .9668902
{txt}{space 19}349  {c |}{col 25}{res}{space 2}-.7795916{col 37}{space 2} .2852375{col 48}{space 1}   -2.73{col 57}{space 3}0.006{col 65}{space 4}-1.338647{col 78}{space 3}-.2205363
{txt}{space 19}350  {c |}{col 25}{res}{space 2}-1.839151{col 37}{space 2} .2862457{col 48}{space 1}   -6.43{col 57}{space 3}0.000{col 65}{space 4}-2.400182{col 78}{space 3} -1.27812
{txt}{space 19}352  {c |}{col 25}{res}{space 2}-.6937714{col 37}{space 2} .3378196{col 48}{space 1}   -2.05{col 57}{space 3}0.040{col 65}{space 4}-1.355886{col 78}{space 3}-.0316572
{txt}{space 19}355  {c |}{col 25}{res}{space 2} -1.01608{col 37}{space 2} .2882421{col 48}{space 1}   -3.53{col 57}{space 3}0.000{col 65}{space 4}-1.581024{col 78}{space 3}-.4511361
{txt}{space 19}360  {c |}{col 25}{res}{space 2}-.5629856{col 37}{space 2} .2380236{col 48}{space 1}   -2.37{col 57}{space 3}0.018{col 65}{space 4}-1.029503{col 78}{space 3}-.0964679
{txt}{space 19}366  {c |}{col 25}{res}{space 2}-.9568396{col 37}{space 2} .3100865{col 48}{space 1}   -3.09{col 57}{space 3}0.002{col 65}{space 4}-1.564598{col 78}{space 3}-.3490812
{txt}{space 19}367  {c |}{col 25}{res}{space 2}-.6069139{col 37}{space 2} .2922114{col 48}{space 1}   -2.08{col 57}{space 3}0.038{col 65}{space 4}-1.179638{col 78}{space 3}-.0341901
{txt}{space 19}368  {c |}{col 25}{res}{space 2}-.6541842{col 37}{space 2} .3244626{col 48}{space 1}   -2.02{col 57}{space 3}0.044{col 65}{space 4}-1.290119{col 78}{space 3}-.0182492
{txt}{space 19}375  {c |}{col 25}{res}{space 2} .4212433{col 37}{space 2} .2666203{col 48}{space 1}    1.58{col 57}{space 3}0.114{col 65}{space 4}-.1013229{col 78}{space 3} .9438094
{txt}{space 19}380  {c |}{col 25}{res}{space 2}-.8761736{col 37}{space 2} .1721794{col 48}{space 1}   -5.09{col 57}{space 3}0.000{col 65}{space 4}-1.213639{col 78}{space 3}-.5387081
{txt}{space 19}390  {c |}{col 25}{res}{space 2}-.6191811{col 37}{space 2} .2649477{col 48}{space 1}   -2.34{col 57}{space 3}0.019{col 65}{space 4}-1.138469{col 78}{space 3}-.0998931
{txt}{space 23} {c |}
{space 18}party {c |}
{space 19}EPP  {c |}{col 25}{res}{space 2} 2.226402{col 37}{space 2} .9432159{col 48}{space 1}    2.36{col 57}{space 3}0.018{col 65}{space 4} .3777328{col 78}{space 3} 4.075071
{txt}{space 19}S&D  {c |}{col 25}{res}{space 2} 2.216256{col 37}{space 2} .9600668{col 48}{space 1}    2.31{col 57}{space 3}0.021{col 65}{space 4} .3345597{col 78}{space 3} 4.097952
{txt}{space 12}Greens/EFA  {c |}{col 25}{res}{space 2} -.752411{col 37}{space 2} .9296071{col 48}{space 1}   -0.81{col 57}{space 3}0.418{col 65}{space 4}-2.574407{col 78}{space 3} 1.069585
{txt}{space 13}ALDE/ADLE  {c |}{col 25}{res}{space 2} 2.314784{col 37}{space 2} .9926525{col 48}{space 1}    2.33{col 57}{space 3}0.020{col 65}{space 4} .3692214{col 78}{space 3} 4.260348
{txt}{space 19}ECR  {c |}{col 25}{res}{space 2} 4.143238{col 37}{space 2} 1.003355{col 48}{space 1}    4.13{col 57}{space 3}0.000{col 65}{space 4} 2.176697{col 78}{space 3} 6.109778
{txt}{space 18}EFDD  {c |}{col 25}{res}{space 2} .2127862{col 37}{space 2} .9439421{col 48}{space 1}    0.23{col 57}{space 3}0.822{col 65}{space 4}-1.637306{col 78}{space 3} 2.062879
{txt}{space 15}GUE-NGL  {c |}{col 25}{res}{space 2}-.9352927{col 37}{space 2} 1.214936{col 48}{space 1}   -0.77{col 57}{space 3}0.441{col 65}{space 4}-3.316523{col 78}{space 3} 1.445937
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2}  -3.4889{col 37}{space 2}   .95128{col 48}{space 1}   -3.67{col 57}{space 3}0.000{col 65}{space 4}-5.353374{col 78}{space 3}-1.624425
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODEL8, title(Model 8)
{txt}
{com}. //Model 9
. logit prosanctions migrants_plustransit_sd population unemployment gdpgrowth distance col libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-637.43518}  
Iteration 2:{space 3}log pseudolikelihood = {res:-629.68242}  
Iteration 3:{space 3}log pseudolikelihood = {res:-629.26818}  
Iteration 4:{space 3}log pseudolikelihood = {res: -629.2629}  
Iteration 5:{space 3}log pseudolikelihood = {res: -629.2629}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(14)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res} -629.2629{txt}{col 49}Pseudo R2{col 67}= {res}    0.4632

{txt}{ralign 89:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}           prosanctions{col 25}{c |}      Coef.{col 37}   Std. Err.{col 49}      z{col 57}   P>|z|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
migrants_plustransit_sd {c |}{col 25}{res}{space 2}-.3142995{col 37}{space 2} .1423402{col 48}{space 1}   -2.21{col 57}{space 3}0.027{col 65}{space 4}-.5932812{col 78}{space 3}-.0353179
{txt}{space 13}population {c |}{col 25}{res}{space 2} .2040573{col 37}{space 2} .2201537{col 48}{space 1}    0.93{col 57}{space 3}0.354{col 65}{space 4} -.227436{col 78}{space 3} .6355505
{txt}{space 11}unemployment {c |}{col 25}{res}{space 2}-.0877465{col 37}{space 2} .0633228{col 48}{space 1}   -1.39{col 57}{space 3}0.166{col 65}{space 4} -.211857{col 78}{space 3}  .036364
{txt}{space 14}gdpgrowth {c |}{col 25}{res}{space 2} .1094903{col 37}{space 2} .1221056{col 48}{space 1}    0.90{col 57}{space 3}0.370{col 65}{space 4}-.1298324{col 78}{space 3} .3488129
{txt}{space 15}distance {c |}{col 25}{res}{space 2} .0000274{col 37}{space 2} .0002147{col 48}{space 1}    0.13{col 57}{space 3}0.898{col 65}{space 4}-.0003933{col 78}{space 3} .0004482
{txt}{space 20}col {c |}{col 25}{res}{space 2} .5618279{col 37}{space 2} .2340781{col 48}{space 1}    2.40{col 57}{space 3}0.016{col 65}{space 4} .1030433{col 78}{space 3} 1.020612
{txt}{space 14}libyabill {c |}{col 25}{res}{space 2}  4.25389{col 37}{space 2} .3791761{col 48}{space 1}   11.22{col 57}{space 3}0.000{col 65}{space 4} 3.510719{col 78}{space 3} 4.997062
{txt}{space 15}iranbill {c |}{col 25}{res}{space 2} 4.352361{col 37}{space 2} .5533876{col 48}{space 1}    7.86{col 57}{space 3}0.000{col 65}{space 4} 3.267741{col 78}{space 3}  5.43698
{txt}{space 23} {c |}
{space 18}ccode {c |}
{space 19}205  {c |}{col 25}{res}{space 2} 10.67609{col 37}{space 2} 13.03102{col 48}{space 1}    0.82{col 57}{space 3}0.413{col 65}{space 4}-14.86424{col 78}{space 3} 36.21643
{txt}{space 19}210  {c |}{col 25}{res}{space 2} 9.980043{col 37}{space 2} 10.03259{col 48}{space 1}    0.99{col 57}{space 3}0.320{col 65}{space 4}-9.683467{col 78}{space 3} 29.64355
{txt}{space 19}211  {c |}{col 25}{res}{space 2} 9.747943{col 37}{space 2} 11.27172{col 48}{space 1}    0.86{col 57}{space 3}0.387{col 65}{space 4}-12.34422{col 78}{space 3} 31.84011
{txt}{space 19}212  {c |}{col 25}{res}{space 2} 11.69507{col 37}{space 2} 13.73927{col 48}{space 1}    0.85{col 57}{space 3}0.395{col 65}{space 4}-15.23341{col 78}{space 3} 38.62355
{txt}{space 19}220  {c |}{col 25}{res}{space 2}-1.270085{col 37}{space 2} .7179172{col 48}{space 1}   -1.77{col 57}{space 3}0.077{col 65}{space 4}-2.677177{col 78}{space 3} .1370064
{txt}{space 19}230  {c |}{col 25}{res}{space 2} 4.163023{col 37}{space 2} 4.156106{col 48}{space 1}    1.00{col 57}{space 3}0.317{col 65}{space 4}-3.982795{col 78}{space 3} 12.30884
{txt}{space 19}235  {c |}{col 25}{res}{space 2} 11.18105{col 37}{space 2} 11.67967{col 48}{space 1}    0.96{col 57}{space 3}0.338{col 65}{space 4}-11.71068{col 78}{space 3} 34.07279
{txt}{space 19}255  {c |}{col 25}{res}{space 2} -3.74499{col 37}{space 2} 3.972545{col 48}{space 1}   -0.94{col 57}{space 3}0.346{col 65}{space 4}-11.53104{col 78}{space 3} 4.041056
{txt}{space 19}290  {c |}{col 25}{res}{space 2} 4.476087{col 37}{space 2} 5.459968{col 48}{space 1}    0.82{col 57}{space 3}0.412{col 65}{space 4}-6.225255{col 78}{space 3} 15.17743
{txt}{space 19}305  {c |}{col 25}{res}{space 2} 10.31532{col 37}{space 2} 11.71815{col 48}{space 1}    0.88{col 57}{space 3}0.379{col 65}{space 4}-12.65183{col 78}{space 3} 33.28247
{txt}{space 19}310  {c |}{col 25}{res}{space 2} 10.15709{col 37}{space 2}  11.6212{col 48}{space 1}    0.87{col 57}{space 3}0.382{col 65}{space 4}-12.62005{col 78}{space 3} 32.93423
{txt}{space 19}316  {c |}{col 25}{res}{space 2} 10.55714{col 37}{space 2} 11.27273{col 48}{space 1}    0.94{col 57}{space 3}0.349{col 65}{space 4}-11.53701{col 78}{space 3} 32.65129
{txt}{space 19}317  {c |}{col 25}{res}{space 2} 11.36879{col 37}{space 2} 12.82276{col 48}{space 1}    0.89{col 57}{space 3}0.375{col 65}{space 4}-13.76337{col 78}{space 3} 36.50094
{txt}{space 19}325  {c |}{col 25}{res}{space 2} .2161093{col 37}{space 2} .7733871{col 48}{space 1}    0.28{col 57}{space 3}0.780{col 65}{space 4}-1.299702{col 78}{space 3}  1.73192
{txt}{space 19}338  {c |}{col 25}{res}{space 2}  11.9988{col 37}{space 2} 13.59968{col 48}{space 1}    0.88{col 57}{space 3}0.378{col 65}{space 4}-14.65608{col 78}{space 3} 38.65369
{txt}{space 19}344  {c |}{col 25}{res}{space 2} 13.42436{col 37}{space 2} 12.96157{col 48}{space 1}    1.04{col 57}{space 3}0.300{col 65}{space 4}-11.97985{col 78}{space 3} 38.82857
{txt}{space 19}349  {c |}{col 25}{res}{space 2} 11.93494{col 37}{space 2} 13.22974{col 48}{space 1}    0.90{col 57}{space 3}0.367{col 65}{space 4}-13.99487{col 78}{space 3} 37.86476
{txt}{space 19}350  {c |}{col 25}{res}{space 2} 10.69881{col 37}{space 2} 11.60765{col 48}{space 1}    0.92{col 57}{space 3}0.357{col 65}{space 4}-12.05177{col 78}{space 3}  33.4494
{txt}{space 19}352  {c |}{col 25}{res}{space 2} 12.91411{col 37}{space 2} 13.27412{col 48}{space 1}    0.97{col 57}{space 3}0.331{col 65}{space 4}-13.10269{col 78}{space 3}  38.9309
{txt}{space 19}355  {c |}{col 25}{res}{space 2} 10.88046{col 37}{space 2} 12.11342{col 48}{space 1}    0.90{col 57}{space 3}0.369{col 65}{space 4}-12.86141{col 78}{space 3} 34.62234
{txt}{space 19}360  {c |}{col 25}{res}{space 2} 8.402087{col 37}{space 2}  9.14834{col 48}{space 1}    0.92{col 57}{space 3}0.358{col 65}{space 4}-9.528331{col 78}{space 3}  26.3325
{txt}{space 19}366  {c |}{col 25}{res}{space 2} 11.90418{col 37}{space 2} 13.57902{col 48}{space 1}    0.88{col 57}{space 3}0.381{col 65}{space 4} -14.7102{col 78}{space 3} 38.51857
{txt}{space 19}367  {c |}{col 25}{res}{space 2} 12.30677{col 37}{space 2} 13.46375{col 48}{space 1}    0.91{col 57}{space 3}0.361{col 65}{space 4}-14.08169{col 78}{space 3} 38.69524
{txt}{space 19}368  {c |}{col 25}{res}{space 2} 12.13082{col 37}{space 2} 13.38538{col 48}{space 1}    0.91{col 57}{space 3}0.365{col 65}{space 4}-14.10406{col 78}{space 3} 38.36569
{txt}{space 19}375  {c |}{col 25}{res}{space 2} 12.40498{col 37}{space 2} 12.65362{col 48}{space 1}    0.98{col 57}{space 3}0.327{col 65}{space 4}-12.39565{col 78}{space 3} 37.20561
{txt}{space 19}380  {c |}{col 25}{res}{space 2} 10.09534{col 37}{space 2}  11.8058{col 48}{space 1}    0.86{col 57}{space 3}0.392{col 65}{space 4}-13.04361{col 78}{space 3} 33.23429
{txt}{space 19}390  {c |}{col 25}{res}{space 2} 11.21994{col 37}{space 2} 12.42377{col 48}{space 1}    0.90{col 57}{space 3}0.366{col 65}{space 4}-13.13021{col 78}{space 3} 35.57009
{txt}{space 23} {c |}
{space 18}party {c |}
{space 19}EPP  {c |}{col 25}{res}{space 2} 2.182565{col 37}{space 2} .9331907{col 48}{space 1}    2.34{col 57}{space 3}0.019{col 65}{space 4} .3535444{col 78}{space 3} 4.011585
{txt}{space 19}S&D  {c |}{col 25}{res}{space 2} 2.171766{col 37}{space 2} .9474651{col 48}{space 1}    2.29{col 57}{space 3}0.022{col 65}{space 4}  .314769{col 78}{space 3} 4.028764
{txt}{space 12}Greens/EFA  {c |}{col 25}{res}{space 2} -.793981{col 37}{space 2} .9212486{col 48}{space 1}   -0.86{col 57}{space 3}0.389{col 65}{space 4}-2.599595{col 78}{space 3} 1.011633
{txt}{space 13}ALDE/ADLE  {c |}{col 25}{res}{space 2} 2.307255{col 37}{space 2} .9838244{col 48}{space 1}    2.35{col 57}{space 3}0.019{col 65}{space 4} .3789944{col 78}{space 3} 4.235515
{txt}{space 19}ECR  {c |}{col 25}{res}{space 2} 4.034499{col 37}{space 2} .9861358{col 48}{space 1}    4.09{col 57}{space 3}0.000{col 65}{space 4} 2.101708{col 78}{space 3}  5.96729
{txt}{space 18}EFDD  {c |}{col 25}{res}{space 2} .0881405{col 37}{space 2} .9034578{col 48}{space 1}    0.10{col 57}{space 3}0.922{col 65}{space 4}-1.682604{col 78}{space 3} 1.858885
{txt}{space 15}GUE-NGL  {c |}{col 25}{res}{space 2}-.9322273{col 37}{space 2} 1.191296{col 48}{space 1}   -0.78{col 57}{space 3}0.434{col 65}{space 4}-3.267125{col 78}{space 3} 1.402671
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2}-16.21316{col 37}{space 2} 13.50886{col 48}{space 1}   -1.20{col 57}{space 3}0.230{col 65}{space 4}-42.69005{col 78}{space 3} 10.26372
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODEL9, title(Model 9)
{txt}
{com}. 
. estout MODEL6 MODEL7 MODEL8 MODEL9, cells(b(star fmt(3)) se(par fmt(3))) starlevels($^{c -(}+{c )-}$ 0.10 $^{c -(}*{c )-}$ 0.05 $^{c -(}**{c )-}$ 0.01 $^{c -(}***{c )-}$ 0.001) stats(N N_g r2, fmt(0 0 3) label(Observations Countries R$^2$)) label,  using "results2.tex", replace style(tex)
{res}{txt}(note: file results2.tex not found)
(output written to {browse  `"results2.tex"'})

{com}. 
. **FIGURE 1
. qui logit prosanctions loggedmigrants population unemployment gdpgrowth distance col libyabill iranbill i.ccode i.party, cluster(ccode)
{txt}
{com}. margins, at(loggedmigrants=(0(1)12)) atmean
{res}
{txt}Adjusted predictions{col 49}Number of obs{col 67}= {res}     1,713
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(prosanctions), predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loggedmigr~s}{space 4}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:population}{space 6}{txt:=} {space 3}38.28121 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:unemployment}{space 4}{txt:=} {space 3}10.22586 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:gdpgrowth}{space 7}{txt:=} {space 4}1.31164 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:distance}{space 8}{txt:=} {space 4}2790.64 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:col}{space 13}{txt:=} {space 3}.0706363 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:libyabill}{space 7}{txt:=} {space 3}.3736135 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:iranbill}{space 8}{txt:=} {space 3}.3000584 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:200.ccode}{space 7}{txt:=} {space 3}.0683012 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:205.ccode}{space 7}{txt:=} {space 3}.0145943 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:210.ccode}{space 7}{txt:=} {space 3}.0402802 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:211.ccode}{space 7}{txt:=} {space 3}.0286048 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:212.ccode}{space 7}{txt:=} {space 3}.0081728 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:220.ccode}{space 7}{txt:=} {space 3}.1027437 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:230.ccode}{space 7}{txt:=} {space 3}.0712201 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:235.ccode}{space 7}{txt:=} {space 3}.0326912 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:255.ccode}{space 7}{txt:=} {space 3}.1371862 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:290.ccode}{space 7}{txt:=} {space 3}.0683012 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:305.ccode}{space 7}{txt:=} {space 3}.0251022 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:310.ccode}{space 7}{txt:=} {space 3}.0315236 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:316.ccode}{space 7}{txt:=} {space 3}.0251022 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:317.ccode}{space 7}{txt:=} {space 3}.0210158 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:distance}{space 8}{txt:=} {space 4}2790.64 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:iranbill}{space 8}{txt:=} {space 3}.3000584 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:205.ccode}{space 7}{txt:=} {space 3}.0145943 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:211.ccode}{space 7}{txt:=} {space 3}.0286048 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:212.ccode}{space 7}{txt:=} {space 3}.0081728 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:255.ccode}{space 7}{txt:=} {space 3}.1371862 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:290.ccode}{space 7}{txt:=} {space 3}.0683012 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:305.ccode}{space 7}{txt:=} {space 3}.0251022 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:310.ccode}{space 7}{txt:=} {space 3}.0315236 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:316.ccode}{space 7}{txt:=} {space 3}.0251022 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:317.ccode}{space 7}{txt:=} {space 3}.0210158 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:325.ccode}{space 7}{txt:=} {space 3}.0974898 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:338.ccode}{space 7}{txt:=} {space 3}.0087566 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:344.ccode}{space 7}{txt:=} {space 3}.0110917 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:349.ccode}{space 7}{txt:=} {space 3}.0116754 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:352.ccode}{space 7}{txt:=} {space 3}.0081728 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:368.ccode}{space 7}{txt:=} {space 3}.0145943 {txt:(mean)}}{p_end}
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{p2col: }{space 2}{res:{txt:375.ccode}{space 7}{txt:=} {space 3}.0175131 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:390.ccode}{space 7}{txt:=} {space 3}.0192644 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:4.party}{space 9}{txt:=} {space 3}.1138354 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:7.party}{space 9}{txt:=} {space 3}.0542907 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{p2col: }{space 2}{res:{txt:211.ccode}{space 7}{txt:=} {space 3}.0286048 {txt:(mean)}}{p_end}
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{p2col: }{space 2}{res:{txt:7.party}{space 9}{txt:=} {space 3}.0542907 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:distance}{space 8}{txt:=} {space 4}2790.64 {txt:(mean)}}{p_end}
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{p2col: }{space 2}{res:{txt:210.ccode}{space 7}{txt:=} {space 3}.0402802 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:366.ccode}{space 7}{txt:=} {space 3}.0093403 {txt:(mean)}}{p_end}
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{p2col: }{space 2}{res:{txt:367.ccode}{space 7}{txt:=} {space 3}.0099241 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:368.ccode}{space 7}{txt:=} {space 3}.0145943 {txt:(mean)}}{p_end}
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{p2col: }{space 2}{res:{txt:375.ccode}{space 7}{txt:=} {space 3}.0175131 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:380.ccode}{space 7}{txt:=} {space 3}.0256859 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:390.ccode}{space 7}{txt:=} {space 3}.0192644 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:0.party}{space 9}{txt:=} {space 3}.0478692 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.party}{space 9}{txt:=} {space 3}.3496789 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:2.party}{space 9}{txt:=} {space 3}.2708698 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:3.party}{space 9}{txt:=} {space 3}.0753065 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:4.party}{space 9}{txt:=} {space 3}.1138354 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:5.party}{space 9}{txt:=} {space 3}.0589609 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:6.party}{space 9}{txt:=} {space 3}.0291886 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:7.party}{space 9}{txt:=} {space 3}.0542907 {txt:(mean)}}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:7._at}:{space 1}{res:{txt:loggedmigr~s}{space 4}{txt:=} {space 10}6}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:population}{space 6}{txt:=} {space 3}38.28121 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:unemployment}{space 4}{txt:=} {space 3}10.22586 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:gdpgrowth}{space 7}{txt:=} {space 4}1.31164 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:distance}{space 8}{txt:=} {space 4}2790.64 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:col}{space 13}{txt:=} {space 3}.0706363 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:libyabill}{space 7}{txt:=} {space 3}.3736135 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:iranbill}{space 8}{txt:=} {space 3}.3000584 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:200.ccode}{space 7}{txt:=} {space 3}.0683012 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:205.ccode}{space 7}{txt:=} {space 3}.0145943 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:210.ccode}{space 7}{txt:=} {space 3}.0402802 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:211.ccode}{space 7}{txt:=} {space 3}.0286048 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:212.ccode}{space 7}{txt:=} {space 3}.0081728 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:220.ccode}{space 7}{txt:=} {space 3}.1027437 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:230.ccode}{space 7}{txt:=} {space 3}.0712201 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:235.ccode}{space 7}{txt:=} {space 3}.0326912 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:255.ccode}{space 7}{txt:=} {space 3}.1371862 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:290.ccode}{space 7}{txt:=} {space 3}.0683012 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:305.ccode}{space 7}{txt:=} {space 3}.0251022 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:310.ccode}{space 7}{txt:=} {space 3}.0315236 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:316.ccode}{space 7}{txt:=} {space 3}.0251022 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:317.ccode}{space 7}{txt:=} {space 3}.0210158 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:325.ccode}{space 7}{txt:=} {space 3}.0974898 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:338.ccode}{space 7}{txt:=} {space 3}.0087566 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:344.ccode}{space 7}{txt:=} {space 3}.0110917 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:349.ccode}{space 7}{txt:=} {space 3}.0116754 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:350.ccode}{space 7}{txt:=} {space 3}.0227671 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:352.ccode}{space 7}{txt:=} {space 3}.0081728 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:355.ccode}{space 7}{txt:=} {space 3}.0256859 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:360.ccode}{space 7}{txt:=} {space 3}.0431991 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:366.ccode}{space 7}{txt:=} {space 3}.0093403 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:367.ccode}{space 7}{txt:=} {space 3}.0099241 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:368.ccode}{space 7}{txt:=} {space 3}.0145943 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:375.ccode}{space 7}{txt:=} {space 3}.0175131 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:380.ccode}{space 7}{txt:=} {space 3}.0256859 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:390.ccode}{space 7}{txt:=} {space 3}.0192644 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:0.party}{space 9}{txt:=} {space 3}.0478692 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.party}{space 9}{txt:=} {space 3}.3496789 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:2.party}{space 9}{txt:=} {space 3}.2708698 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:3.party}{space 9}{txt:=} {space 3}.0753065 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:4.party}{space 9}{txt:=} {space 3}.1138354 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:5.party}{space 9}{txt:=} {space 3}.0589609 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:6.party}{space 9}{txt:=} {space 3}.0291886 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:7.party}{space 9}{txt:=} {space 3}.0542907 {txt:(mean)}}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:8._at}:{space 1}{res:{txt:loggedmigr~s}{space 4}{txt:=} {space 10}7}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:population}{space 6}{txt:=} {space 3}38.28121 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:unemployment}{space 4}{txt:=} {space 3}10.22586 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:gdpgrowth}{space 7}{txt:=} {space 4}1.31164 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:distance}{space 8}{txt:=} {space 4}2790.64 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:col}{space 13}{txt:=} {space 3}.0706363 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:libyabill}{space 7}{txt:=} {space 3}.3736135 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:iranbill}{space 8}{txt:=} {space 3}.3000584 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:200.ccode}{space 7}{txt:=} {space 3}.0683012 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:205.ccode}{space 7}{txt:=} {space 3}.0145943 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:210.ccode}{space 7}{txt:=} {space 3}.0402802 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:211.ccode}{space 7}{txt:=} {space 3}.0286048 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:212.ccode}{space 7}{txt:=} {space 3}.0081728 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:220.ccode}{space 7}{txt:=} {space 3}.1027437 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:230.ccode}{space 7}{txt:=} {space 3}.0712201 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:235.ccode}{space 7}{txt:=} {space 3}.0326912 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:255.ccode}{space 7}{txt:=} {space 3}.1371862 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:290.ccode}{space 7}{txt:=} {space 3}.0683012 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:305.ccode}{space 7}{txt:=} {space 3}.0251022 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:310.ccode}{space 7}{txt:=} {space 3}.0315236 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:316.ccode}{space 7}{txt:=} {space 3}.0251022 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:317.ccode}{space 7}{txt:=} {space 3}.0210158 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:325.ccode}{space 7}{txt:=} {space 3}.0974898 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:338.ccode}{space 7}{txt:=} {space 3}.0087566 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:344.ccode}{space 7}{txt:=} {space 3}.0110917 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:349.ccode}{space 7}{txt:=} {space 3}.0116754 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:350.ccode}{space 7}{txt:=} {space 3}.0227671 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:352.ccode}{space 7}{txt:=} {space 3}.0081728 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:355.ccode}{space 7}{txt:=} {space 3}.0256859 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:360.ccode}{space 7}{txt:=} {space 3}.0431991 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:366.ccode}{space 7}{txt:=} {space 3}.0093403 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:367.ccode}{space 7}{txt:=} {space 3}.0099241 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:368.ccode}{space 7}{txt:=} {space 3}.0145943 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:375.ccode}{space 7}{txt:=} {space 3}.0175131 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:380.ccode}{space 7}{txt:=} {space 3}.0256859 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:390.ccode}{space 7}{txt:=} {space 3}.0192644 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:0.party}{space 9}{txt:=} {space 3}.0478692 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.party}{space 9}{txt:=} {space 3}.3496789 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:2.party}{space 9}{txt:=} {space 3}.2708698 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:3.party}{space 9}{txt:=} {space 3}.0753065 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:4.party}{space 9}{txt:=} {space 3}.1138354 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:5.party}{space 9}{txt:=} {space 3}.0589609 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:6.party}{space 9}{txt:=} {space 3}.0291886 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:7.party}{space 9}{txt:=} {space 3}.0542907 {txt:(mean)}}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:9._at}:{space 1}{res:{txt:loggedmigr~s}{space 4}{txt:=} {space 10}8}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:population}{space 6}{txt:=} {space 3}38.28121 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:unemployment}{space 4}{txt:=} {space 3}10.22586 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:gdpgrowth}{space 7}{txt:=} {space 4}1.31164 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:distance}{space 8}{txt:=} {space 4}2790.64 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:col}{space 13}{txt:=} {space 3}.0706363 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:libyabill}{space 7}{txt:=} {space 3}.3736135 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:iranbill}{space 8}{txt:=} {space 3}.3000584 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:200.ccode}{space 7}{txt:=} {space 3}.0683012 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:205.ccode}{space 7}{txt:=} {space 3}.0145943 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:210.ccode}{space 7}{txt:=} {space 3}.0402802 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:211.ccode}{space 7}{txt:=} {space 3}.0286048 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:212.ccode}{space 7}{txt:=} {space 3}.0081728 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:220.ccode}{space 7}{txt:=} {space 3}.1027437 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:230.ccode}{space 7}{txt:=} {space 3}.0712201 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:235.ccode}{space 7}{txt:=} {space 3}.0326912 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:255.ccode}{space 7}{txt:=} {space 3}.1371862 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:290.ccode}{space 7}{txt:=} {space 3}.0683012 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:305.ccode}{space 7}{txt:=} {space 3}.0251022 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:310.ccode}{space 7}{txt:=} {space 3}.0315236 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:316.ccode}{space 7}{txt:=} {space 3}.0251022 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:317.ccode}{space 7}{txt:=} {space 3}.0210158 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:325.ccode}{space 7}{txt:=} {space 3}.0974898 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:338.ccode}{space 7}{txt:=} {space 3}.0087566 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:344.ccode}{space 7}{txt:=} {space 3}.0110917 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:349.ccode}{space 7}{txt:=} {space 3}.0116754 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:350.ccode}{space 7}{txt:=} {space 3}.0227671 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:352.ccode}{space 7}{txt:=} {space 3}.0081728 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:355.ccode}{space 7}{txt:=} {space 3}.0256859 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:360.ccode}{space 7}{txt:=} {space 3}.0431991 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:366.ccode}{space 7}{txt:=} {space 3}.0093403 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:367.ccode}{space 7}{txt:=} {space 3}.0099241 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:368.ccode}{space 7}{txt:=} {space 3}.0145943 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:375.ccode}{space 7}{txt:=} {space 3}.0175131 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:380.ccode}{space 7}{txt:=} {space 3}.0256859 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:390.ccode}{space 7}{txt:=} {space 3}.0192644 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:0.party}{space 9}{txt:=} {space 3}.0478692 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.party}{space 9}{txt:=} {space 3}.3496789 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:2.party}{space 9}{txt:=} {space 3}.2708698 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:3.party}{space 9}{txt:=} {space 3}.0753065 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:4.party}{space 9}{txt:=} {space 3}.1138354 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:5.party}{space 9}{txt:=} {space 3}.0589609 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:6.party}{space 9}{txt:=} {space 3}.0291886 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:7.party}{space 9}{txt:=} {space 3}.0542907 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:205.ccode}{space 7}{txt:=} {space 3}.0145943 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:211.ccode}{space 7}{txt:=} {space 3}.0286048 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:212.ccode}{space 7}{txt:=} {space 3}.0081728 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:220.ccode}{space 7}{txt:=} {space 3}.1027437 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:230.ccode}{space 7}{txt:=} {space 3}.0712201 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:235.ccode}{space 7}{txt:=} {space 3}.0326912 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:255.ccode}{space 7}{txt:=} {space 3}.1371862 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:290.ccode}{space 7}{txt:=} {space 3}.0683012 {txt:(mean)}}{p_end}
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{p2col: }{space 2}{res:{txt:305.ccode}{space 7}{txt:=} {space 3}.0251022 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:310.ccode}{space 7}{txt:=} {space 3}.0315236 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:316.ccode}{space 7}{txt:=} {space 3}.0251022 {txt:(mean)}}{p_end}
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{p2col: }{space 2}{res:{txt:317.ccode}{space 7}{txt:=} {space 3}.0210158 {txt:(mean)}}{p_end}
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{p2col: }{space 2}{res:{txt:325.ccode}{space 7}{txt:=} {space 3}.0974898 {txt:(mean)}}{p_end}
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{p2col: }{space 2}{res:{txt:338.ccode}{space 7}{txt:=} {space 3}.0087566 {txt:(mean)}}{p_end}
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{p2col: }{space 2}{res:{txt:344.ccode}{space 7}{txt:=} {space 3}.0110917 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{p2col: }{space 2}{res:{txt:350.ccode}{space 7}{txt:=} {space 3}.0227671 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:355.ccode}{space 7}{txt:=} {space 3}.0256859 {txt:(mean)}}{p_end}
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{p2col: }{space 2}{res:{txt:367.ccode}{space 7}{txt:=} {space 3}.0099241 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:368.ccode}{space 7}{txt:=} {space 3}.0145943 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:375.ccode}{space 7}{txt:=} {space 3}.0175131 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:380.ccode}{space 7}{txt:=} {space 3}.0256859 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:390.ccode}{space 7}{txt:=} {space 3}.0192644 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{p2col: }{space 2}{res:{txt:5.party}{space 9}{txt:=} {space 3}.0589609 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:6.party}{space 9}{txt:=} {space 3}.0291886 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:7.party}{space 9}{txt:=} {space 3}.0542907 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:distance}{space 8}{txt:=} {space 4}2790.64 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:col}{space 13}{txt:=} {space 3}.0706363 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{p2col: }{space 2}{res:{txt:210.ccode}{space 7}{txt:=} {space 3}.0402802 {txt:(mean)}}{p_end}
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{p2col: }{space 2}{res:{txt:212.ccode}{space 7}{txt:=} {space 3}.0081728 {txt:(mean)}}{p_end}
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{p2col: }{space 2}{res:{txt:230.ccode}{space 7}{txt:=} {space 3}.0712201 {txt:(mean)}}{p_end}
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{p2col: }{space 2}{res:{txt:235.ccode}{space 7}{txt:=} {space 3}.0326912 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:255.ccode}{space 7}{txt:=} {space 3}.1371862 {txt:(mean)}}{p_end}
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{p2col: }{space 2}{res:{txt:290.ccode}{space 7}{txt:=} {space 3}.0683012 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{p2col: }{space 2}{res:{txt:310.ccode}{space 7}{txt:=} {space 3}.0315236 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:316.ccode}{space 7}{txt:=} {space 3}.0251022 {txt:(mean)}}{p_end}
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{p2col: }{space 2}{res:{txt:317.ccode}{space 7}{txt:=} {space 3}.0210158 {txt:(mean)}}{p_end}
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{p2col: }{space 2}{res:{txt:325.ccode}{space 7}{txt:=} {space 3}.0974898 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:338.ccode}{space 7}{txt:=} {space 3}.0087566 {txt:(mean)}}{p_end}
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{p2col: }{space 2}{res:{txt:344.ccode}{space 7}{txt:=} {space 3}.0110917 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:349.ccode}{space 7}{txt:=} {space 3}.0116754 {txt:(mean)}}{p_end}
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{p2col: }{space 2}{res:{txt:350.ccode}{space 7}{txt:=} {space 3}.0227671 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:355.ccode}{space 7}{txt:=} {space 3}.0256859 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:366.ccode}{space 7}{txt:=} {space 3}.0093403 {txt:(mean)}}{p_end}
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{p2col: }{space 2}{res:{txt:367.ccode}{space 7}{txt:=} {space 3}.0099241 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:368.ccode}{space 7}{txt:=} {space 3}.0145943 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:375.ccode}{space 7}{txt:=} {space 3}.0175131 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:380.ccode}{space 7}{txt:=} {space 3}.0256859 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:390.ccode}{space 7}{txt:=} {space 3}.0192644 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
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{p2col: }{space 2}{res:{txt:3.party}{space 9}{txt:=} {space 3}.0753065 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:5.party}{space 9}{txt:=} {space 3}.0589609 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:6.party}{space 9}{txt:=} {space 3}.0291886 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:7.party}{space 9}{txt:=} {space 3}.0542907 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:unemployment}{space 4}{txt:=} {space 3}10.22586 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:gdpgrowth}{space 7}{txt:=} {space 4}1.31164 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:distance}{space 8}{txt:=} {space 4}2790.64 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:col}{space 13}{txt:=} {space 3}.0706363 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:iranbill}{space 8}{txt:=} {space 3}.3000584 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:205.ccode}{space 7}{txt:=} {space 3}.0145943 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:210.ccode}{space 7}{txt:=} {space 3}.0402802 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:211.ccode}{space 7}{txt:=} {space 3}.0286048 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:212.ccode}{space 7}{txt:=} {space 3}.0081728 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:220.ccode}{space 7}{txt:=} {space 3}.1027437 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:230.ccode}{space 7}{txt:=} {space 3}.0712201 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:235.ccode}{space 7}{txt:=} {space 3}.0326912 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:255.ccode}{space 7}{txt:=} {space 3}.1371862 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:290.ccode}{space 7}{txt:=} {space 3}.0683012 {txt:(mean)}}{p_end}
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{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:305.ccode}{space 7}{txt:=} {space 3}.0251022 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
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{txt}{p2colset 1 14 16 2}{...}
{p2col:13._at}:{space 1}{res:{txt:loggedmigr~s}{space 4}{txt:=} {space 9}12}{p_end}
{p2colreset}{...}
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{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .8947061{col 26}{space 2}  .061199{col 37}{space 1}   14.62{col 46}{space 3}0.000{col 54}{space 4} .7747582{col 67}{space 3} 1.014654
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .8711959{col 26}{space 2} .0631238{col 37}{space 1}   13.80{col 46}{space 3}0.000{col 54}{space 4} .7474754{col 67}{space 3} .9949163
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .8433553{col 26}{space 2} .0628168{col 37}{space 1}   13.43{col 46}{space 3}0.000{col 54}{space 4} .7202367{col 67}{space 3}  .966474
{txt}{space 10}4  {c |}{col 14}{res}{space 2}  .810804{col 26}{space 2} .0596051{col 37}{space 1}   13.60{col 46}{space 3}0.000{col 54}{space 4} .6939802{col 67}{space 3} .9276278
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .7733066{col 26}{space 2} .0529046{col 37}{space 1}   14.62{col 46}{space 3}0.000{col 54}{space 4} .6696154{col 67}{space 3} .8769978
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .7308446{col 26}{space 2} .0423717{col 37}{space 1}   17.25{col 46}{space 3}0.000{col 54}{space 4} .6477975{col 67}{space 3} .8138916
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .6836823{col 26}{space 2} .0281475{col 37}{space 1}   24.29{col 46}{space 3}0.000{col 54}{space 4} .6285142{col 67}{space 3} .7388504
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .6324126{col 26}{space 2} .0120901{col 37}{space 1}   52.31{col 46}{space 3}0.000{col 54}{space 4} .6087163{col 67}{space 3} .6561088
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .5779626{col 26}{space 2} .0148189{col 37}{space 1}   39.00{col 46}{space 3}0.000{col 54}{space 4} .5489181{col 67}{space 3} .6070071
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .5215491{col 26}{space 2} .0352001{col 37}{space 1}   14.82{col 46}{space 3}0.000{col 54}{space 4} .4525582{col 67}{space 3}   .59054
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .4645805{col 26}{space 2} .0563549{col 37}{space 1}    8.24{col 46}{space 3}0.000{col 54}{space 4} .3541268{col 67}{space 3} .5750341
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .4085214{col 26}{space 2} .0756345{col 37}{space 1}    5.40{col 46}{space 3}0.000{col 54}{space 4} .2602804{col 67}{space 3} .5567623
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .3547449{col 26}{space 2} .0915179{col 37}{space 1}    3.88{col 46}{space 3}0.000{col 54}{space 4} .1753731{col 67}{space 3} .5341166
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot, xsize(10) ysize(9) addplot(hist loggedmigrants if e(sample), yaxis(2) xscale(range(0(1)12)) xlabel(0(1)12) yscale(range(0(.1)1)) ylabel() yscale(axis(2) range(0 .75) off) lc(white%0) fcolor(sand%25) recast(rbar)) recast(line) recastci(rarea) ciopts(fc(blue%30) lc(white%0)) xtitle(ln(Migrant Stock)) title("") ytitle(Predicted Probability of Voting in Favor of Sanctions) legend(off) graphregion(fcolor(white) lcolor(white))
{res}
{text}{p 2 6 2}Variables that uniquely identify margins: loggedmigrants{p_end}
{res}{txt}
{com}. 
. ********************************************************************************
. **********************************APPENDIX**************************************
. ********************************************************************************
. 
. **TABLE A1
. qui logit prosanctions loggedmigrants, cluster(ccode)
{txt}
{com}. sutex prosanctions loggedmigrants migrantshare migrantshare2 ln_transitstock_weighted migrants_plustransit_sd ln_borderdetect libyabill iranbill population unemp gdpgrowth distance col immigrantpolicy RWPvoteshare  pincometax sstax  exportsGDP importsGDP logged_refugee lnremit if e(sample), minmax nobs
%------- Begin LaTeX code -------%

\begin{table}[htbp]\centering \caption{Summary statistics \label{sumstat}}
\begin{tabular}{l c c c c c}\hline\hline
\multicolumn{1}{c}{\textbf{Variable}} & \textbf{Mean}
 & \textbf{Std. Dev.}& \textbf{Min.} &  \textbf{Max.} & \textbf{N}\\ \hline
prosanctions & 0.566 & 0.496 & 0 & 1 & 1713\\
loggedmigrants & 7.374 & 3.023 & 0 & 11.701 & 1713\\
migrantshare & 0.039 & 0.083 & 0 & 0.698 & 1713\\
migrantshare2 & 0.456 & 0.882 & 0 & 4.964 & 1713\\
ln\_transitstock\_weighted & 10.799 & 2.612 & 4.707 & 13.727 & 640\\
migrants\_plustransit\_sd & -0.012 & 1.341 & -0.988 & 4.073 & 1713\\
ln\_borderdetect & 1.14 & 1.812 & 0 & 6.485 & 1713\\
libyabill & 0.374 & 0.484 & 0 & 1 & 1713\\
iranbill & 0.3 & 0.458 & 0 & 1 & 1713\\
population & 38.281 & 28.689 & 0.415 & 81.777 & 1713\\
unemp & 10.281 & 5.536 & 5 & 26.5 & 1713\\
gdpgrowth & 1.312 & 1.826 & -5.939 & 5.691 & 1713\\
distance & 2790.64 & 955.403 & 373.986 & 5343.777 & 1713\\
col & 0.071 & 0.256 & 0 & 1 & 1713\\
immigrantpolicy & 2.477 & 0.204 & 1.798 & 2.986 & 1713\\
RWPvoteshare & 5.704 & 6.328 & 0 & 28.2 & 1713\\
pincometax & 8.602 & 3.645 & 2.769 & 28.168 & 1522\\
sstax & 12.628 & 3.663 & 0.33 & 18.604 & 1522\\
exportsGDP & 0 & 0.001 & 0 & 0.008 & 1699\\
importsGDP & 0 & 0 & 0 & 0.002 & 1699\\
logged\_refugees & 4.964 & 2.541 & 0.693 & 9.75 & 1621\\
lnremit & 2.312 & 1.468 & 0 & 4.871 & 1576\\
\hline
\end{tabular}
\end{table}
%------- End LaTeX code -------%
{txt}
{com}. 
. **TABLE A2
. //Model A1
. logit prosanctions loggedmigrants population unemployment gdpgrowth distance col immigrantpolicy libyabill iranbill i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-650.65142}  
Iteration 2:{space 3}log pseudolikelihood = {res:-644.42551}  
Iteration 3:{space 3}log pseudolikelihood = {res:-644.07434}  
Iteration 4:{space 3}log pseudolikelihood = {res:-644.07066}  
Iteration 5:{space 3}log pseudolikelihood = {res:-644.07066}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}Wald chi2({res}16{txt}){col 67}= {res}    726.29
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-644.07066{txt}{col 49}Pseudo R2{col 67}= {res}    0.4506

{txt}{ralign 81:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}   prosanctions{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}loggedmigrants {c |}{col 17}{res}{space 2}-.1086774{col 29}{space 2}  .033611{col 40}{space 1}   -3.23{col 49}{space 3}0.001{col 57}{space 4}-.1745538{col 70}{space 3} -.042801
{txt}{space 5}population {c |}{col 17}{res}{space 2}-.0006238{col 29}{space 2} .0037614{col 40}{space 1}   -0.17{col 49}{space 3}0.868{col 57}{space 4}-.0079959{col 70}{space 3} .0067484
{txt}{space 3}unemployment {c |}{col 17}{res}{space 2}-.0525385{col 29}{space 2} .0214941{col 40}{space 1}   -2.44{col 49}{space 3}0.015{col 57}{space 4}-.0946663{col 70}{space 3}-.0104108
{txt}{space 6}gdpgrowth {c |}{col 17}{res}{space 2} .0103021{col 29}{space 2} .0621562{col 40}{space 1}    0.17{col 49}{space 3}0.868{col 57}{space 4}-.1115219{col 70}{space 3}  .132126
{txt}{space 7}distance {c |}{col 17}{res}{space 2}  .000228{col 29}{space 2} .0001103{col 40}{space 1}    2.07{col 49}{space 3}0.039{col 57}{space 4} .0000119{col 70}{space 3} .0004442
{txt}{space 12}col {c |}{col 17}{res}{space 2} .1912057{col 29}{space 2} .1806148{col 40}{space 1}    1.06{col 49}{space 3}0.290{col 57}{space 4}-.1627928{col 70}{space 3} .5452041
{txt}immigrantpolicy {c |}{col 17}{res}{space 2} .1028389{col 29}{space 2} .2538877{col 40}{space 1}    0.41{col 49}{space 3}0.685{col 57}{space 4}-.3947718{col 70}{space 3} .6004497
{txt}{space 6}libyabill {c |}{col 17}{res}{space 2} 3.785875{col 29}{space 2} .3160528{col 40}{space 1}   11.98{col 49}{space 3}0.000{col 57}{space 4} 3.166423{col 70}{space 3} 4.405327
{txt}{space 7}iranbill {c |}{col 17}{res}{space 2} 3.726265{col 29}{space 2} .3894637{col 40}{space 1}    9.57{col 49}{space 3}0.000{col 57}{space 4}  2.96293{col 70}{space 3} 4.489599
{txt}{space 15} {c |}
{space 10}party {c |}
{space 11}EPP  {c |}{col 17}{res}{space 2} 2.252965{col 29}{space 2} .9350766{col 40}{space 1}    2.41{col 49}{space 3}0.016{col 57}{space 4} .4202489{col 70}{space 3} 4.085682
{txt}{space 11}S&D  {c |}{col 17}{res}{space 2} 2.279906{col 29}{space 2} .9484381{col 40}{space 1}    2.40{col 49}{space 3}0.016{col 57}{space 4} .4210011{col 70}{space 3}  4.13881
{txt}{space 4}Greens/EFA  {c |}{col 17}{res}{space 2}-.7600329{col 29}{space 2} .9205801{col 40}{space 1}   -0.83{col 49}{space 3}0.409{col 57}{space 4}-2.564337{col 70}{space 3} 1.044271
{txt}{space 5}ALDE/ADLE  {c |}{col 17}{res}{space 2} 2.376075{col 29}{space 2} .9472198{col 40}{space 1}    2.51{col 49}{space 3}0.012{col 57}{space 4} .5195584{col 70}{space 3} 4.232592
{txt}{space 11}ECR  {c |}{col 17}{res}{space 2}  4.19021{col 29}{space 2} 1.029809{col 40}{space 1}    4.07{col 49}{space 3}0.000{col 57}{space 4} 2.171821{col 70}{space 3} 6.208598
{txt}{space 10}EFDD  {c |}{col 17}{res}{space 2} .2955967{col 29}{space 2} .9339434{col 40}{space 1}    0.32{col 49}{space 3}0.752{col 57}{space 4}-1.534899{col 70}{space 3} 2.126092
{txt}{space 7}GUE-NGL  {c |}{col 17}{res}{space 2}-.7565702{col 29}{space 2} 1.132962{col 40}{space 1}   -0.67{col 49}{space 3}0.504{col 57}{space 4}-2.977134{col 70}{space 3} 1.463994
{txt}{space 15} {c |}
{space 10}_cons {c |}{col 17}{res}{space 2}-3.487457{col 29}{space 2} 1.164785{col 40}{space 1}   -2.99{col 49}{space 3}0.003{col 57}{space 4}-5.770393{col 70}{space 3}-1.204521
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA1, title((A1))
{txt}
{com}. //Model A2
. logit prosanctions loggedmigrants population unemployment gdpgrowth distance col RWPvoteshare libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-633.63195}  
Iteration 2:{space 3}log pseudolikelihood = {res:-625.77118}  
Iteration 3:{space 3}log pseudolikelihood = {res:-625.38244}  
Iteration 4:{space 3}log pseudolikelihood = {res:-625.37927}  
Iteration 5:{space 3}log pseudolikelihood = {res:-625.37926}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(15)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-625.37926{txt}{col 49}Pseudo R2{col 67}= {res}    0.4665

{txt}{ralign 80:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}  prosanctions{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
loggedmigrants {c |}{col 16}{res}{space 2} -.200254{col 28}{space 2} .0856765{col 39}{space 1}   -2.34{col 48}{space 3}0.019{col 56}{space 4}-.3681769{col 69}{space 3}-.0323312
{txt}{space 4}population {c |}{col 16}{res}{space 2} .2862043{col 28}{space 2} .2164474{col 39}{space 1}    1.32{col 48}{space 3}0.186{col 56}{space 4}-.1380248{col 69}{space 3} .7104334
{txt}{space 2}unemployment {c |}{col 16}{res}{space 2}-.0958443{col 28}{space 2} .0471231{col 39}{space 1}   -2.03{col 48}{space 3}0.042{col 56}{space 4}-.1882038{col 69}{space 3}-.0034847
{txt}{space 5}gdpgrowth {c |}{col 16}{res}{space 2} .0877947{col 28}{space 2} .0964068{col 39}{space 1}    0.91{col 48}{space 3}0.362{col 56}{space 4}-.1011592{col 69}{space 3} .2767486
{txt}{space 6}distance {c |}{col 16}{res}{space 2} .0005561{col 28}{space 2} .0002511{col 39}{space 1}    2.21{col 48}{space 3}0.027{col 56}{space 4} .0000639{col 69}{space 3} .0010483
{txt}{space 11}col {c |}{col 16}{res}{space 2} .4134858{col 28}{space 2} .4061067{col 39}{space 1}    1.02{col 48}{space 3}0.309{col 56}{space 4}-.3824687{col 69}{space 3}  1.20944
{txt}{space 2}RWPvoteshare {c |}{col 16}{res}{space 2}-.0687695{col 28}{space 2} .0442811{col 39}{space 1}   -1.55{col 48}{space 3}0.120{col 56}{space 4}-.1555588{col 69}{space 3} .0180198
{txt}{space 5}libyabill {c |}{col 16}{res}{space 2} 4.002187{col 28}{space 2} .4102352{col 39}{space 1}    9.76{col 48}{space 3}0.000{col 56}{space 4} 3.198141{col 69}{space 3} 4.806233
{txt}{space 6}iranbill {c |}{col 16}{res}{space 2} 3.763838{col 28}{space 2}  .377124{col 39}{space 1}    9.98{col 48}{space 3}0.000{col 56}{space 4} 3.024688{col 69}{space 3} 4.502987
{txt}{space 14} {c |}
{space 9}ccode {c |}
{space 10}205  {c |}{col 16}{res}{space 2} 15.66024{col 28}{space 2} 12.71583{col 39}{space 1}    1.23{col 48}{space 3}0.218{col 56}{space 4}-9.262322{col 69}{space 3} 40.58281
{txt}{space 10}210  {c |}{col 16}{res}{space 2} 15.22489{col 28}{space 2} 10.05241{col 39}{space 1}    1.51{col 48}{space 3}0.130{col 56}{space 4}-4.477477{col 69}{space 3} 34.92725
{txt}{space 10}211  {c |}{col 16}{res}{space 2} 14.52882{col 28}{space 2} 11.01361{col 39}{space 1}    1.32{col 48}{space 3}0.187{col 56}{space 4}-7.057454{col 69}{space 3} 36.11509
{txt}{space 10}212  {c |}{col 16}{res}{space 2} 16.39943{col 28}{space 2} 13.26363{col 39}{space 1}    1.24{col 48}{space 3}0.216{col 56}{space 4}-9.596802{col 69}{space 3} 42.39567
{txt}{space 10}220  {c |}{col 16}{res}{space 2}-.1294053{col 28}{space 2} .8437127{col 39}{space 1}   -0.15{col 48}{space 3}0.878{col 56}{space 4}-1.783052{col 69}{space 3} 1.524241
{txt}{space 10}230  {c |}{col 16}{res}{space 2} 5.573701{col 28}{space 2} 3.765393{col 39}{space 1}    1.48{col 48}{space 3}0.139{col 56}{space 4}-1.806334{col 69}{space 3} 12.95374
{txt}{space 10}235  {c |}{col 16}{res}{space 2} 14.95815{col 28}{space 2}  11.1476{col 39}{space 1}    1.34{col 48}{space 3}0.180{col 56}{space 4}-6.890741{col 69}{space 3} 36.80703
{txt}{space 10}255  {c |}{col 16}{res}{space 2}-4.589449{col 28}{space 2} 3.765678{col 39}{space 1}   -1.22{col 48}{space 3}0.223{col 56}{space 4}-11.97004{col 69}{space 3} 2.791143
{txt}{space 10}290  {c |}{col 16}{res}{space 2} 7.170641{col 28}{space 2} 5.479169{col 39}{space 1}    1.31{col 48}{space 3}0.191{col 56}{space 4}-3.568332{col 69}{space 3} 17.90961
{txt}{space 10}305  {c |}{col 16}{res}{space 2} 17.46971{col 28}{space 2} 11.85397{col 39}{space 1}    1.47{col 48}{space 3}0.141{col 56}{space 4}-5.763644{col 69}{space 3} 40.70307
{txt}{space 10}310  {c |}{col 16}{res}{space 2}  16.7947{col 28}{space 2}  11.6609{col 39}{space 1}    1.44{col 48}{space 3}0.150{col 56}{space 4}-6.060242{col 69}{space 3} 39.64963
{txt}{space 10}316  {c |}{col 16}{res}{space 2} 15.97486{col 28}{space 2} 11.25092{col 39}{space 1}    1.42{col 48}{space 3}0.156{col 56}{space 4}-6.076538{col 69}{space 3} 38.02626
{txt}{space 10}317  {c |}{col 16}{res}{space 2} 17.02056{col 28}{space 2} 12.55461{col 39}{space 1}    1.36{col 48}{space 3}0.175{col 56}{space 4}-7.586018{col 69}{space 3} 41.62714
{txt}{space 10}325  {c |}{col 16}{res}{space 2} 1.958155{col 28}{space 2} .9309406{col 39}{space 1}    2.10{col 48}{space 3}0.035{col 56}{space 4} .1335454{col 69}{space 3} 3.782765
{txt}{space 10}338  {c |}{col 16}{res}{space 2}  18.1943{col 28}{space 2} 13.69341{col 39}{space 1}    1.33{col 48}{space 3}0.184{col 56}{space 4}-8.644285{col 69}{space 3} 45.03289
{txt}{space 10}344  {c |}{col 16}{res}{space 2} 18.52387{col 28}{space 2} 12.49718{col 39}{space 1}    1.48{col 48}{space 3}0.138{col 56}{space 4} -5.97016{col 69}{space 3}  43.0179
{txt}{space 10}349  {c |}{col 16}{res}{space 2} 17.46468{col 28}{space 2}  13.0464{col 39}{space 1}    1.34{col 48}{space 3}0.181{col 56}{space 4}  -8.1058{col 69}{space 3} 43.03515
{txt}{space 10}350  {c |}{col 16}{res}{space 2}  17.1932{col 28}{space 2} 11.58663{col 39}{space 1}    1.48{col 48}{space 3}0.138{col 56}{space 4}-5.516171{col 69}{space 3} 39.90258
{txt}{space 10}352  {c |}{col 16}{res}{space 2} 19.49503{col 28}{space 2} 13.48133{col 39}{space 1}    1.45{col 48}{space 3}0.148{col 56}{space 4}-6.927895{col 69}{space 3} 45.91796
{txt}{space 10}355  {c |}{col 16}{res}{space 2} 17.28962{col 28}{space 2} 12.14771{col 39}{space 1}    1.42{col 48}{space 3}0.155{col 56}{space 4}-6.519448{col 69}{space 3} 41.09869
{txt}{space 10}360  {c |}{col 16}{res}{space 2} 13.20074{col 28}{space 2} 9.345599{col 39}{space 1}    1.41{col 48}{space 3}0.158{col 56}{space 4}-5.116294{col 69}{space 3} 31.51778
{txt}{space 10}366  {c |}{col 16}{res}{space 2} 16.17589{col 28}{space 2} 12.94476{col 39}{space 1}    1.25{col 48}{space 3}0.211{col 56}{space 4}-9.195378{col 69}{space 3} 41.54716
{txt}{space 10}367  {c |}{col 16}{res}{space 2} 18.28125{col 28}{space 2} 13.06169{col 39}{space 1}    1.40{col 48}{space 3}0.162{col 56}{space 4}-7.319183{col 69}{space 3} 43.88168
{txt}{space 10}368  {c |}{col 16}{res}{space 2} 16.92819{col 28}{space 2} 12.92927{col 39}{space 1}    1.31{col 48}{space 3}0.190{col 56}{space 4} -8.41272{col 69}{space 3} 42.26909
{txt}{space 10}375  {c |}{col 16}{res}{space 2} 18.05193{col 28}{space 2} 12.41265{col 39}{space 1}    1.45{col 48}{space 3}0.146{col 56}{space 4}-6.276416{col 69}{space 3} 42.38027
{txt}{space 10}380  {c |}{col 16}{res}{space 2} 15.63476{col 28}{space 2} 11.72188{col 39}{space 1}    1.33{col 48}{space 3}0.182{col 56}{space 4}-7.339705{col 69}{space 3} 38.60923
{txt}{space 10}390  {c |}{col 16}{res}{space 2} 17.38014{col 28}{space 2} 12.35059{col 39}{space 1}    1.41{col 48}{space 3}0.159{col 56}{space 4}-6.826571{col 69}{space 3} 41.58686
{txt}{space 14} {c |}
{space 9}party {c |}
{space 10}EPP  {c |}{col 16}{res}{space 2} 2.188199{col 28}{space 2} .9502001{col 39}{space 1}    2.30{col 48}{space 3}0.021{col 56}{space 4} .3258414{col 69}{space 3} 4.050557
{txt}{space 10}S&D  {c |}{col 16}{res}{space 2} 2.193849{col 28}{space 2} .9687305{col 39}{space 1}    2.26{col 48}{space 3}0.024{col 56}{space 4} .2951725{col 69}{space 3} 4.092526
{txt}{space 3}Greens/EFA  {c |}{col 16}{res}{space 2}-.7589676{col 28}{space 2} .9666594{col 39}{space 1}   -0.79{col 48}{space 3}0.432{col 56}{space 4}-2.653585{col 69}{space 3}  1.13565
{txt}{space 4}ALDE/ADLE  {c |}{col 16}{res}{space 2} 2.341007{col 28}{space 2} 1.002681{col 39}{space 1}    2.33{col 48}{space 3}0.020{col 56}{space 4} .3757887{col 69}{space 3} 4.306225
{txt}{space 10}ECR  {c |}{col 16}{res}{space 2}  4.00656{col 28}{space 2} .9941788{col 39}{space 1}    4.03{col 48}{space 3}0.000{col 56}{space 4} 2.058005{col 69}{space 3} 5.955114
{txt}{space 9}EFDD  {c |}{col 16}{res}{space 2} .1219093{col 28}{space 2} .9285558{col 39}{space 1}    0.13{col 48}{space 3}0.896{col 56}{space 4}-1.698026{col 69}{space 3} 1.941845
{txt}{space 6}GUE-NGL  {c |}{col 16}{res}{space 2}-.9389747{col 28}{space 2} 1.242029{col 39}{space 1}   -0.76{col 48}{space 3}0.450{col 56}{space 4}-3.373307{col 69}{space 3} 1.495357
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2}-21.53434{col 28}{space 2} 13.52237{col 39}{space 1}   -1.59{col 48}{space 3}0.111{col 56}{space 4}-48.03771{col 69}{space 3} 4.969022
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA2, title((A2))
{txt}
{com}. //Model A3
. logit prosanctions loggedmigrants population unemployment gdpgrowth distance col exportsGDP importsGDP libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1162.7147}  
Iteration 1:{space 3}log pseudolikelihood = {res:-628.85054}  
Iteration 2:{space 3}log pseudolikelihood = {res:-620.84329}  
Iteration 3:{space 3}log pseudolikelihood = {res:-620.44168}  
Iteration 4:{space 3}log pseudolikelihood = {res: -620.4383}  
Iteration 5:{space 3}log pseudolikelihood = {res: -620.4383}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,699
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(16)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res} -620.4383{txt}{col 49}Pseudo R2{col 67}= {res}    0.4664

{txt}{ralign 80:(Std. Err. adjusted for {res:27} clusters in ccode)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}  prosanctions{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
loggedmigrants {c |}{col 16}{res}{space 2}-.2307852{col 28}{space 2} .0994619{col 39}{space 1}   -2.32{col 48}{space 3}0.020{col 56}{space 4}-.4257269{col 69}{space 3}-.0358434
{txt}{space 4}population {c |}{col 16}{res}{space 2} .4136248{col 28}{space 2} .2293468{col 39}{space 1}    1.80{col 48}{space 3}0.071{col 56}{space 4}-.0358867{col 69}{space 3} .8631362
{txt}{space 2}unemployment {c |}{col 16}{res}{space 2} -.067963{col 28}{space 2} .0574159{col 39}{space 1}   -1.18{col 48}{space 3}0.237{col 56}{space 4}-.1804961{col 69}{space 3} .0445702
{txt}{space 5}gdpgrowth {c |}{col 16}{res}{space 2} .1271759{col 28}{space 2} .1132269{col 39}{space 1}    1.12{col 48}{space 3}0.261{col 56}{space 4}-.0947447{col 69}{space 3} .3490966
{txt}{space 6}distance {c |}{col 16}{res}{space 2} .0005407{col 28}{space 2} .0003433{col 39}{space 1}    1.57{col 48}{space 3}0.115{col 56}{space 4}-.0001322{col 69}{space 3} .0012136
{txt}{space 11}col {c |}{col 16}{res}{space 2} .8797333{col 28}{space 2} .3167133{col 39}{space 1}    2.78{col 48}{space 3}0.005{col 56}{space 4} .2589867{col 69}{space 3}  1.50048
{txt}{space 4}exportsGDP {c |}{col 16}{res}{space 2} 250.0449{col 28}{space 2} 202.5478{col 39}{space 1}    1.23{col 48}{space 3}0.217{col 56}{space 4}-146.9414{col 69}{space 3} 647.0312
{txt}{space 4}importsGDP {c |}{col 16}{res}{space 2}-834.3956{col 28}{space 2} 532.5244{col 39}{space 1}   -1.57{col 48}{space 3}0.117{col 56}{space 4}-1878.124{col 69}{space 3} 209.3331
{txt}{space 5}libyabill {c |}{col 16}{res}{space 2} 3.966187{col 28}{space 2} .3345556{col 39}{space 1}   11.86{col 48}{space 3}0.000{col 56}{space 4}  3.31047{col 69}{space 3} 4.621904
{txt}{space 6}iranbill {c |}{col 16}{res}{space 2} 3.618852{col 28}{space 2} .4169756{col 39}{space 1}    8.68{col 48}{space 3}0.000{col 56}{space 4} 2.801595{col 69}{space 3} 4.436109
{txt}{space 14} {c |}
{space 9}ccode {c |}
{space 10}205  {c |}{col 16}{res}{space 2} 23.32059{col 28}{space 2} 13.47297{col 39}{space 1}    1.73{col 48}{space 3}0.083{col 56}{space 4}-3.085939{col 69}{space 3} 49.72712
{txt}{space 10}210  {c |}{col 16}{res}{space 2} 20.70414{col 28}{space 2} 10.64953{col 39}{space 1}    1.94{col 48}{space 3}0.052{col 56}{space 4}-.1685539{col 69}{space 3} 41.57684
{txt}{space 10}211  {c |}{col 16}{res}{space 2} 20.64188{col 28}{space 2} 11.69072{col 39}{space 1}    1.77{col 48}{space 3}0.077{col 56}{space 4} -2.27151{col 69}{space 3} 43.55527
{txt}{space 10}220  {c |}{col 16}{res}{space 2}-1.219485{col 28}{space 2} .5687593{col 39}{space 1}   -2.14{col 48}{space 3}0.032{col 56}{space 4}-2.334233{col 69}{space 3}-.1047375
{txt}{space 10}230  {c |}{col 16}{res}{space 2} 7.349094{col 28}{space 2} 4.115325{col 39}{space 1}    1.79{col 48}{space 3}0.074{col 56}{space 4}-.7167953{col 69}{space 3} 15.41498
{txt}{space 10}235  {c |}{col 16}{res}{space 2} 21.39754{col 28}{space 2}  11.8315{col 39}{space 1}    1.81{col 48}{space 3}0.071{col 56}{space 4}-1.791781{col 69}{space 3} 44.58686
{txt}{space 10}255  {c |}{col 16}{res}{space 2}-6.862515{col 28}{space 2} 3.970767{col 39}{space 1}   -1.73{col 48}{space 3}0.084{col 56}{space 4}-14.64507{col 69}{space 3} .9200447
{txt}{space 10}290  {c |}{col 16}{res}{space 2} 10.15348{col 28}{space 2} 5.857394{col 39}{space 1}    1.73{col 48}{space 3}0.083{col 56}{space 4}-1.326802{col 69}{space 3} 21.63376
{txt}{space 10}305  {c |}{col 16}{res}{space 2} 23.22119{col 28}{space 2} 12.54153{col 39}{space 1}    1.85{col 48}{space 3}0.064{col 56}{space 4}-1.359763{col 69}{space 3} 47.80214
{txt}{space 10}310  {c |}{col 16}{res}{space 2} 22.14006{col 28}{space 2} 12.42873{col 39}{space 1}    1.78{col 48}{space 3}0.075{col 56}{space 4}-2.219806{col 69}{space 3} 46.49992
{txt}{space 10}316  {c |}{col 16}{res}{space 2} 22.15142{col 28}{space 2} 11.95211{col 39}{space 1}    1.85{col 48}{space 3}0.064{col 56}{space 4}-1.274278{col 69}{space 3} 45.57712
{txt}{space 10}317  {c |}{col 16}{res}{space 2} 23.57137{col 28}{space 2} 13.42658{col 39}{space 1}    1.76{col 48}{space 3}0.079{col 56}{space 4}-2.744244{col 69}{space 3} 49.88698
{txt}{space 10}325  {c |}{col 16}{res}{space 2} 2.069976{col 28}{space 2}  .976865{col 39}{space 1}    2.12{col 48}{space 3}0.034{col 56}{space 4} .1553557{col 69}{space 3} 3.984596
{txt}{space 10}338  {c |}{col 16}{res}{space 2} 25.31598{col 28}{space 2} 14.44908{col 39}{space 1}    1.75{col 48}{space 3}0.080{col 56}{space 4}-3.003701{col 69}{space 3} 53.63567
{txt}{space 10}344  {c |}{col 16}{res}{space 2} 25.58901{col 28}{space 2} 13.33225{col 39}{space 1}    1.92{col 48}{space 3}0.055{col 56}{space 4}-.5417303{col 69}{space 3} 51.71975
{txt}{space 10}349  {c |}{col 16}{res}{space 2} 24.74646{col 28}{space 2} 13.83974{col 39}{space 1}    1.79{col 48}{space 3}0.074{col 56}{space 4}-2.378926{col 69}{space 3} 51.87185
{txt}{space 10}350  {c |}{col 16}{res}{space 2} 23.35189{col 28}{space 2} 12.43013{col 39}{space 1}    1.88{col 48}{space 3}0.060{col 56}{space 4}-1.010719{col 69}{space 3}  47.7145
{txt}{space 10}352  {c |}{col 16}{res}{space 2} 27.36703{col 28}{space 2} 14.38638{col 39}{space 1}    1.90{col 48}{space 3}0.057{col 56}{space 4}-.8297492{col 69}{space 3} 55.56382
{txt}{space 10}355  {c |}{col 16}{res}{space 2} 23.55681{col 28}{space 2} 12.94391{col 39}{space 1}    1.82{col 48}{space 3}0.069{col 56}{space 4} -1.81278{col 69}{space 3}  48.9264
{txt}{space 10}360  {c |}{col 16}{res}{space 2} 18.37057{col 28}{space 2} 9.961924{col 39}{space 1}    1.84{col 48}{space 3}0.065{col 56}{space 4}-1.154439{col 69}{space 3} 37.89558
{txt}{space 10}366  {c |}{col 16}{res}{space 2}  23.7177{col 28}{space 2} 13.73456{col 39}{space 1}    1.73{col 48}{space 3}0.084{col 56}{space 4}-3.201538{col 69}{space 3} 50.63693
{txt}{space 10}367  {c |}{col 16}{res}{space 2}  24.4163{col 28}{space 2} 13.89439{col 39}{space 1}    1.76{col 48}{space 3}0.079{col 56}{space 4}-2.816206{col 69}{space 3} 51.64881
{txt}{space 10}368  {c |}{col 16}{res}{space 2} 23.86985{col 28}{space 2} 13.73932{col 39}{space 1}    1.74{col 48}{space 3}0.082{col 56}{space 4}-3.058721{col 69}{space 3} 50.79842
{txt}{space 10}375  {c |}{col 16}{res}{space 2} 24.61233{col 28}{space 2} 13.20408{col 39}{space 1}    1.86{col 48}{space 3}0.062{col 56}{space 4}-1.267195{col 69}{space 3} 50.49186
{txt}{space 10}380  {c |}{col 16}{res}{space 2} 21.88157{col 28}{space 2} 12.46376{col 39}{space 1}    1.76{col 48}{space 3}0.079{col 56}{space 4}-2.546943{col 69}{space 3} 46.31009
{txt}{space 10}390  {c |}{col 16}{res}{space 2} 23.86112{col 28}{space 2} 13.11462{col 39}{space 1}    1.82{col 48}{space 3}0.069{col 56}{space 4}-1.843054{col 69}{space 3}  49.5653
{txt}{space 14} {c |}
{space 9}party {c |}
{space 10}EPP  {c |}{col 16}{res}{space 2}  2.15542{col 28}{space 2}  .956392{col 39}{space 1}    2.25{col 48}{space 3}0.024{col 56}{space 4} .2809261{col 69}{space 3} 4.029914
{txt}{space 10}S&D  {c |}{col 16}{res}{space 2} 2.151624{col 28}{space 2} .9715365{col 39}{space 1}    2.21{col 48}{space 3}0.027{col 56}{space 4} .2474476{col 69}{space 3} 4.055801
{txt}{space 3}Greens/EFA  {c |}{col 16}{res}{space 2}-.8082117{col 28}{space 2} .9621616{col 39}{space 1}   -0.84{col 48}{space 3}0.401{col 56}{space 4}-2.694014{col 69}{space 3}  1.07759
{txt}{space 4}ALDE/ADLE  {c |}{col 16}{res}{space 2} 2.238578{col 28}{space 2} 1.003114{col 39}{space 1}    2.23{col 48}{space 3}0.026{col 56}{space 4} .2725103{col 69}{space 3} 4.204646
{txt}{space 10}ECR  {c |}{col 16}{res}{space 2}  3.98946{col 28}{space 2} .9876843{col 39}{space 1}    4.04{col 48}{space 3}0.000{col 56}{space 4} 2.053634{col 69}{space 3} 5.925285
{txt}{space 9}EFDD  {c |}{col 16}{res}{space 2} .0773578{col 28}{space 2} .9280476{col 39}{space 1}    0.08{col 48}{space 3}0.934{col 56}{space 4}-1.741582{col 69}{space 3} 1.896298
{txt}{space 6}GUE-NGL  {c |}{col 16}{res}{space 2}-.9721527{col 28}{space 2} 1.240663{col 39}{space 1}   -0.78{col 48}{space 3}0.433{col 56}{space 4}-3.403807{col 69}{space 3} 1.459502
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2}-29.45583{col 28}{space 2}  14.3683{col 39}{space 1}   -2.05{col 48}{space 3}0.040{col 56}{space 4}-57.61718{col 69}{space 3}-1.294473
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA3, title((A3))
{txt}
{com}. //Model A4
. logit prosanctions loggedmigrants population unemployment gdpgrowth distance col pincometax sstax libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1045.4561}  
Iteration 1:{space 3}log pseudolikelihood = {res: -593.1858}  
Iteration 2:{space 3}log pseudolikelihood = {res:-587.28805}  
Iteration 3:{space 3}log pseudolikelihood = {res:-586.93778}  
Iteration 4:{space 3}log pseudolikelihood = {res:-586.93549}  
Iteration 5:{space 3}log pseudolikelihood = {res:-586.93549}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,522
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(16)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-586.93549{txt}{col 49}Pseudo R2{col 67}= {res}    0.4386

{txt}{ralign 80:(Std. Err. adjusted for {res:22} clusters in ccode)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}  prosanctions{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
loggedmigrants {c |}{col 16}{res}{space 2}-.1937965{col 28}{space 2} .0889436{col 39}{space 1}   -2.18{col 48}{space 3}0.029{col 56}{space 4}-.3681227{col 69}{space 3}-.0194702
{txt}{space 4}population {c |}{col 16}{res}{space 2}  .246852{col 28}{space 2} .2108071{col 39}{space 1}    1.17{col 48}{space 3}0.242{col 56}{space 4}-.1663223{col 69}{space 3} .6600263
{txt}{space 2}unemployment {c |}{col 16}{res}{space 2}-.0271749{col 28}{space 2} .0567336{col 39}{space 1}   -0.48{col 48}{space 3}0.632{col 56}{space 4}-.1383707{col 69}{space 3} .0840209
{txt}{space 5}gdpgrowth {c |}{col 16}{res}{space 2}-.0226997{col 28}{space 2} .1279792{col 39}{space 1}   -0.18{col 48}{space 3}0.859{col 56}{space 4}-.2735343{col 69}{space 3} .2281349
{txt}{space 6}distance {c |}{col 16}{res}{space 2} .0003546{col 28}{space 2} .0003249{col 39}{space 1}    1.09{col 48}{space 3}0.275{col 56}{space 4}-.0002821{col 69}{space 3} .0009914
{txt}{space 11}col {c |}{col 16}{res}{space 2} .4509374{col 28}{space 2} .5870038{col 39}{space 1}    0.77{col 48}{space 3}0.442{col 56}{space 4}-.6995689{col 69}{space 3} 1.601444
{txt}{space 4}pincometax {c |}{col 16}{res}{space 2}-.3106582{col 28}{space 2} .2536434{col 39}{space 1}   -1.22{col 48}{space 3}0.221{col 56}{space 4}-.8077902{col 69}{space 3} .1864737
{txt}{space 9}sstax {c |}{col 16}{res}{space 2} .0008604{col 28}{space 2} .5905899{col 39}{space 1}    0.00{col 48}{space 3}0.999{col 56}{space 4}-1.156675{col 69}{space 3} 1.158395
{txt}{space 5}libyabill {c |}{col 16}{res}{space 2} 3.697413{col 28}{space 2} .4162451{col 39}{space 1}    8.88{col 48}{space 3}0.000{col 56}{space 4} 2.881588{col 69}{space 3} 4.513239
{txt}{space 6}iranbill {c |}{col 16}{res}{space 2} 3.668205{col 28}{space 2} .3949264{col 39}{space 1}    9.29{col 48}{space 3}0.000{col 56}{space 4} 2.894163{col 69}{space 3} 4.442247
{txt}{space 14} {c |}
{space 9}ccode {c |}
{space 10}205  {c |}{col 16}{res}{space 2} 13.09249{col 28}{space 2} 12.54509{col 39}{space 1}    1.04{col 48}{space 3}0.297{col 56}{space 4}-11.49543{col 69}{space 3}  37.6804
{txt}{space 10}210  {c |}{col 16}{res}{space 2} 11.79333{col 28}{space 2} 9.258872{col 39}{space 1}    1.27{col 48}{space 3}0.203{col 56}{space 4}-6.353729{col 69}{space 3} 29.94038
{txt}{space 10}211  {c |}{col 16}{res}{space 2} 12.91572{col 28}{space 2} 10.08344{col 39}{space 1}    1.28{col 48}{space 3}0.200{col 56}{space 4}-6.847455{col 69}{space 3} 32.67889
{txt}{space 10}212  {c |}{col 16}{res}{space 2} 14.04554{col 28}{space 2} 12.16885{col 39}{space 1}    1.15{col 48}{space 3}0.248{col 56}{space 4}-9.804973{col 69}{space 3} 37.89605
{txt}{space 10}220  {c |}{col 16}{res}{space 2}-1.531517{col 28}{space 2} 7.167579{col 39}{space 1}   -0.21{col 48}{space 3}0.831{col 56}{space 4}-15.57971{col 69}{space 3} 12.51668
{txt}{space 10}230  {c |}{col 16}{res}{space 2} 2.989359{col 28}{space 2} 4.135751{col 39}{space 1}    0.72{col 48}{space 3}0.470{col 56}{space 4}-5.116564{col 69}{space 3} 11.09528
{txt}{space 10}235  {c |}{col 16}{res}{space 2} 11.60053{col 28}{space 2}  10.5807{col 39}{space 1}    1.10{col 48}{space 3}0.273{col 56}{space 4}-9.137266{col 69}{space 3} 32.33833
{txt}{space 10}255  {c |}{col 16}{res}{space 2}-4.138333{col 28}{space 2} 6.750277{col 39}{space 1}   -0.61{col 48}{space 3}0.540{col 56}{space 4}-17.36863{col 69}{space 3} 9.091967
{txt}{space 10}290  {c |}{col 16}{res}{space 2} 4.479773{col 28}{space 2} 5.437526{col 39}{space 1}    0.82{col 48}{space 3}0.410{col 56}{space 4}-6.177582{col 69}{space 3} 15.13713
{txt}{space 10}305  {c |}{col 16}{res}{space 2} 13.67064{col 28}{space 2}  11.0267{col 39}{space 1}    1.24{col 48}{space 3}0.215{col 56}{space 4}-7.941294{col 69}{space 3} 35.28257
{txt}{space 10}310  {c |}{col 16}{res}{space 2} 12.01497{col 28}{space 2} 10.86085{col 39}{space 1}    1.11{col 48}{space 3}0.269{col 56}{space 4}-9.271918{col 69}{space 3} 33.30185
{txt}{space 10}316  {c |}{col 16}{res}{space 2} 11.39451{col 28}{space 2} 10.67656{col 39}{space 1}    1.07{col 48}{space 3}0.286{col 56}{space 4}-9.531162{col 69}{space 3} 32.32018
{txt}{space 10}317  {c |}{col 16}{res}{space 2} 11.80852{col 28}{space 2} 11.95599{col 39}{space 1}    0.99{col 48}{space 3}0.323{col 56}{space 4}-11.62479{col 69}{space 3} 35.24183
{txt}{space 10}325  {c |}{col 16}{res}{space 2} 1.431635{col 28}{space 2} 4.064396{col 39}{space 1}    0.35{col 48}{space 3}0.725{col 56}{space 4}-6.534434{col 69}{space 3} 9.397704
{txt}{space 10}349  {c |}{col 16}{res}{space 2} 13.24191{col 28}{space 2} 12.20379{col 39}{space 1}    1.09{col 48}{space 3}0.278{col 56}{space 4}-10.67707{col 69}{space 3}  37.1609
{txt}{space 10}350  {c |}{col 16}{res}{space 2} 11.31289{col 28}{space 2} 11.34254{col 39}{space 1}    1.00{col 48}{space 3}0.319{col 56}{space 4}-10.91809{col 69}{space 3} 33.54387
{txt}{space 10}366  {c |}{col 16}{res}{space 2} 12.34453{col 28}{space 2} 12.12383{col 39}{space 1}    1.02{col 48}{space 3}0.309{col 56}{space 4}-11.41773{col 69}{space 3} 36.10679
{txt}{space 10}367  {c |}{col 16}{res}{space 2}  13.3999{col 28}{space 2} 12.59648{col 39}{space 1}    1.06{col 48}{space 3}0.287{col 56}{space 4}-11.28875{col 69}{space 3} 38.08855
{txt}{space 10}375  {c |}{col 16}{res}{space 2} 15.85997{col 28}{space 2} 11.28932{col 39}{space 1}    1.40{col 48}{space 3}0.160{col 56}{space 4}-6.266698{col 69}{space 3} 37.98664
{txt}{space 10}380  {c |}{col 16}{res}{space 2} 13.82943{col 28}{space 2} 10.74619{col 39}{space 1}    1.29{col 48}{space 3}0.198{col 56}{space 4}-7.232718{col 69}{space 3} 34.89158
{txt}{space 10}390  {c |}{col 16}{res}{space 2} 19.25537{col 28}{space 2}  12.8203{col 39}{space 1}    1.50{col 48}{space 3}0.133{col 56}{space 4} -5.87195{col 69}{space 3} 44.38269
{txt}{space 14} {c |}
{space 9}party {c |}
{space 10}EPP  {c |}{col 16}{res}{space 2} 2.289784{col 28}{space 2}  1.00515{col 39}{space 1}    2.28{col 48}{space 3}0.023{col 56}{space 4} .3197257{col 69}{space 3} 4.259843
{txt}{space 10}S&D  {c |}{col 16}{res}{space 2} 2.284586{col 28}{space 2} 1.016766{col 39}{space 1}    2.25{col 48}{space 3}0.025{col 56}{space 4} .2917616{col 69}{space 3}  4.27741
{txt}{space 3}Greens/EFA  {c |}{col 16}{res}{space 2}-.5843287{col 28}{space 2} 1.012784{col 39}{space 1}   -0.58{col 48}{space 3}0.564{col 56}{space 4} -2.56935{col 69}{space 3} 1.400692
{txt}{space 4}ALDE/ADLE  {c |}{col 16}{res}{space 2} 2.478928{col 28}{space 2} 1.068824{col 39}{space 1}    2.32{col 48}{space 3}0.020{col 56}{space 4} .3840711{col 69}{space 3} 4.573785
{txt}{space 10}ECR  {c |}{col 16}{res}{space 2} 4.174321{col 28}{space 2} 1.032969{col 39}{space 1}    4.04{col 48}{space 3}0.000{col 56}{space 4}  2.14974{col 69}{space 3} 6.198902
{txt}{space 9}EFDD  {c |}{col 16}{res}{space 2} .1837562{col 28}{space 2} .9590512{col 39}{space 1}    0.19{col 48}{space 3}0.848{col 56}{space 4} -1.69595{col 69}{space 3} 2.063462
{txt}{space 6}GUE-NGL  {c |}{col 16}{res}{space 2} -.783653{col 28}{space 2} 1.301535{col 39}{space 1}   -0.60{col 48}{space 3}0.547{col 56}{space 4}-3.334616{col 69}{space 3}  1.76731
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2}-15.78843{col 28}{space 2} 15.79566{col 39}{space 1}   -1.00{col 48}{space 3}0.318{col 56}{space 4}-46.74735{col 69}{space 3} 15.17049
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA4, title((A4))
{txt}
{com}. //Model A5
. logit prosanctions loggedmigrants population unemployment gdpgrowth distance col logged_refugee libyabill iranbill i.ccode i.party, cluster(ccode)

{txt}note: 349.ccode != 0 predicts success perfectly
      349.ccode dropped and 6 obs not used

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1106.6407}  
Iteration 1:{space 3}log pseudolikelihood = {res:-594.94073}  
Iteration 2:{space 3}log pseudolikelihood = {res:-587.34885}  
Iteration 3:{space 3}log pseudolikelihood = {res:-586.98094}  
Iteration 4:{space 3}log pseudolikelihood = {res:-586.97792}  
Iteration 5:{space 3}log pseudolikelihood = {res:-586.97792}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,615
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(15)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-586.97792{txt}{col 49}Pseudo R2{col 67}= {res}    0.4696

{txt}{ralign 81:(Std. Err. adjusted for {res:25} clusters in ccode)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}   prosanctions{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}loggedmigrants {c |}{col 17}{res}{space 2}-.2428046{col 29}{space 2} .1290413{col 40}{space 1}   -1.88{col 49}{space 3}0.060{col 57}{space 4} -.495721{col 70}{space 3} .0101117
{txt}{space 5}population {c |}{col 17}{res}{space 2} .3957735{col 29}{space 2} .2477749{col 40}{space 1}    1.60{col 49}{space 3}0.110{col 57}{space 4}-.0898564{col 70}{space 3} .8814035
{txt}{space 3}unemployment {c |}{col 17}{res}{space 2}-.1142117{col 29}{space 2} .0652266{col 40}{space 1}   -1.75{col 49}{space 3}0.080{col 57}{space 4}-.2420534{col 70}{space 3} .0136301
{txt}{space 6}gdpgrowth {c |}{col 17}{res}{space 2} .1265938{col 29}{space 2} .1386917{col 40}{space 1}    0.91{col 49}{space 3}0.361{col 57}{space 4} -.145237{col 70}{space 3} .3984247
{txt}{space 7}distance {c |}{col 17}{res}{space 2} .0005516{col 29}{space 2} .0003229{col 40}{space 1}    1.71{col 49}{space 3}0.088{col 57}{space 4}-.0000813{col 70}{space 3} .0011845
{txt}{space 12}col {c |}{col 17}{res}{space 2}  .912535{col 29}{space 2} .3603964{col 40}{space 1}    2.53{col 49}{space 3}0.011{col 57}{space 4} .2061709{col 70}{space 3} 1.618899
{txt}logged_refugees {c |}{col 17}{res}{space 2}-.0098513{col 29}{space 2} .1545155{col 40}{space 1}   -0.06{col 49}{space 3}0.949{col 57}{space 4}-.3126962{col 70}{space 3} .2929935
{txt}{space 6}libyabill {c |}{col 17}{res}{space 2} 3.778413{col 29}{space 2} .4017122{col 40}{space 1}    9.41{col 49}{space 3}0.000{col 57}{space 4} 2.991072{col 70}{space 3} 4.565755
{txt}{space 7}iranbill {c |}{col 17}{res}{space 2} 3.862235{col 29}{space 2} .4037159{col 40}{space 1}    9.57{col 49}{space 3}0.000{col 57}{space 4} 3.070967{col 70}{space 3} 4.653504
{txt}{space 15} {c |}
{space 10}ccode {c |}
{space 11}205  {c |}{col 17}{res}{space 2}  22.0642{col 29}{space 2} 14.62379{col 40}{space 1}    1.51{col 49}{space 3}0.131{col 57}{space 4}  -6.5979{col 70}{space 3} 50.72629
{txt}{space 11}210  {c |}{col 17}{res}{space 2} 19.51705{col 29}{space 2} 11.49655{col 40}{space 1}    1.70{col 49}{space 3}0.090{col 57}{space 4}-3.015774{col 70}{space 3} 42.04988
{txt}{space 11}211  {c |}{col 17}{res}{space 2} 19.65461{col 29}{space 2} 12.72269{col 40}{space 1}    1.54{col 49}{space 3}0.122{col 57}{space 4}-5.281409{col 70}{space 3} 44.59063
{txt}{space 11}212  {c |}{col 17}{res}{space 2} 23.04225{col 29}{space 2} 15.37912{col 40}{space 1}    1.50{col 49}{space 3}0.134{col 57}{space 4} -7.10027{col 70}{space 3} 53.18478
{txt}{space 11}220  {c |}{col 17}{res}{space 2}-1.272445{col 29}{space 2}  .503616{col 40}{space 1}   -2.53{col 49}{space 3}0.012{col 57}{space 4}-2.259514{col 70}{space 3}-.2853755
{txt}{space 11}230  {c |}{col 17}{res}{space 2} 7.605516{col 29}{space 2} 4.602886{col 40}{space 1}    1.65{col 49}{space 3}0.098{col 57}{space 4}-1.415976{col 70}{space 3} 16.62701
{txt}{space 11}235  {c |}{col 17}{res}{space 2}  20.7964{col 29}{space 2} 12.95597{col 40}{space 1}    1.61{col 49}{space 3}0.108{col 57}{space 4}-4.596831{col 70}{space 3} 46.18963
{txt}{space 11}255  {c |}{col 17}{res}{space 2}-6.728832{col 29}{space 2} 4.257185{col 40}{space 1}   -1.58{col 49}{space 3}0.114{col 57}{space 4}-15.07276{col 70}{space 3} 1.615097
{txt}{space 11}290  {c |}{col 17}{res}{space 2} 9.690234{col 29}{space 2} 6.587436{col 40}{space 1}    1.47{col 49}{space 3}0.141{col 57}{space 4}-3.220904{col 70}{space 3} 22.60137
{txt}{space 11}305  {c |}{col 17}{res}{space 2} 21.66025{col 29}{space 2} 13.68032{col 40}{space 1}    1.58{col 49}{space 3}0.113{col 57}{space 4}-5.152683{col 70}{space 3} 48.47319
{txt}{space 11}310  {c |}{col 17}{res}{space 2} 21.23207{col 29}{space 2} 13.65793{col 40}{space 1}    1.55{col 49}{space 3}0.120{col 57}{space 4}-5.536981{col 70}{space 3} 48.00113
{txt}{space 11}316  {c |}{col 17}{res}{space 2} 21.14694{col 29}{space 2} 13.08246{col 40}{space 1}    1.62{col 49}{space 3}0.106{col 57}{space 4}-4.494209{col 70}{space 3} 46.78808
{txt}{space 11}317  {c |}{col 17}{res}{space 2} 22.68579{col 29}{space 2} 14.75307{col 40}{space 1}    1.54{col 49}{space 3}0.124{col 57}{space 4}-6.229702{col 70}{space 3} 51.60129
{txt}{space 11}325  {c |}{col 17}{res}{space 2} 1.888076{col 29}{space 2} 1.173586{col 40}{space 1}    1.61{col 49}{space 3}0.108{col 57}{space 4}-.4121106{col 70}{space 3} 4.188264
{txt}{space 11}338  {c |}{col 17}{res}{space 2} 24.83863{col 29}{space 2} 15.96008{col 40}{space 1}    1.56{col 49}{space 3}0.120{col 57}{space 4}-6.442549{col 70}{space 3}  56.1198
{txt}{space 11}349  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (empty)
{space 11}350  {c |}{col 17}{res}{space 2} 22.47162{col 29}{space 2} 13.70985{col 40}{space 1}    1.64{col 49}{space 3}0.101{col 57}{space 4}-4.399194{col 70}{space 3} 49.34244
{txt}{space 11}352  {c |}{col 17}{res}{space 2} 26.46526{col 29}{space 2} 15.77679{col 40}{space 1}    1.68{col 49}{space 3}0.093{col 57}{space 4}-4.456673{col 70}{space 3}  57.3872
{txt}{space 11}355  {c |}{col 17}{res}{space 2} 22.58959{col 29}{space 2} 14.28049{col 40}{space 1}    1.58{col 49}{space 3}0.114{col 57}{space 4}-5.399655{col 70}{space 3} 50.57883
{txt}{space 11}360  {c |}{col 17}{res}{space 2}  17.6637{col 29}{space 2} 11.02042{col 40}{space 1}    1.60{col 49}{space 3}0.109{col 57}{space 4}-3.935939{col 70}{space 3} 39.26333
{txt}{space 11}367  {c |}{col 17}{res}{space 2} 23.17733{col 29}{space 2}  15.3091{col 40}{space 1}    1.51{col 49}{space 3}0.130{col 57}{space 4}-6.827949{col 70}{space 3}  53.1826
{txt}{space 11}368  {c |}{col 17}{res}{space 2} 22.55608{col 29}{space 2} 15.17602{col 40}{space 1}    1.49{col 49}{space 3}0.137{col 57}{space 4}-7.188369{col 70}{space 3} 52.30054
{txt}{space 11}375  {c |}{col 17}{res}{space 2} 23.60765{col 29}{space 2} 14.34377{col 40}{space 1}    1.65{col 49}{space 3}0.100{col 57}{space 4}-4.505628{col 70}{space 3} 51.72092
{txt}{space 11}380  {c |}{col 17}{res}{space 2} 20.96654{col 29}{space 2} 13.51941{col 40}{space 1}    1.55{col 49}{space 3}0.121{col 57}{space 4}-5.531015{col 70}{space 3} 47.46409
{txt}{space 11}390  {c |}{col 17}{res}{space 2} 22.80808{col 29}{space 2} 14.21728{col 40}{space 1}    1.60{col 49}{space 3}0.109{col 57}{space 4}-5.057274{col 70}{space 3} 50.67344
{txt}{space 15} {c |}
{space 10}party {c |}
{space 11}EPP  {c |}{col 17}{res}{space 2} 2.181544{col 29}{space 2}  .961906{col 40}{space 1}    2.27{col 49}{space 3}0.023{col 57}{space 4} .2962426{col 70}{space 3} 4.066845
{txt}{space 11}S&D  {c |}{col 17}{res}{space 2} 2.137934{col 29}{space 2} .9808704{col 40}{space 1}    2.18{col 49}{space 3}0.029{col 57}{space 4} .2154633{col 70}{space 3} 4.060405
{txt}{space 4}Greens/EFA  {c |}{col 17}{res}{space 2}-.9221133{col 29}{space 2}  .980303{col 40}{space 1}   -0.94{col 49}{space 3}0.347{col 57}{space 4}-2.843472{col 70}{space 3} .9992453
{txt}{space 5}ALDE/ADLE  {c |}{col 17}{res}{space 2} 2.223825{col 29}{space 2} 1.019668{col 40}{space 1}    2.18{col 49}{space 3}0.029{col 57}{space 4} .2253127{col 70}{space 3} 4.222337
{txt}{space 11}ECR  {c |}{col 17}{res}{space 2} 3.926459{col 29}{space 2} 1.013089{col 40}{space 1}    3.88{col 49}{space 3}0.000{col 57}{space 4} 1.940842{col 70}{space 3} 5.912076
{txt}{space 10}EFDD  {c |}{col 17}{res}{space 2}-.0119776{col 29}{space 2} .9208752{col 40}{space 1}   -0.01{col 49}{space 3}0.990{col 57}{space 4} -1.81686{col 70}{space 3} 1.792905
{txt}{space 7}GUE-NGL  {c |}{col 17}{res}{space 2} -1.29426{col 29}{space 2} 1.291221{col 40}{space 1}   -1.00{col 49}{space 3}0.316{col 57}{space 4}-3.825007{col 70}{space 3} 1.236487
{txt}{space 15} {c |}
{space 10}_cons {c |}{col 17}{res}{space 2}-27.80905{col 29}{space 2} 15.69464{col 40}{space 1}   -1.77{col 49}{space 3}0.076{col 57}{space 4}-58.56997{col 70}{space 3} 2.951875
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA5, title((A5))
{txt}
{com}. //Model A6
. logit prosanctions loggedmigrants population unemployment gdpgrowth distance col lnremit libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1087.5167}  
Iteration 1:{space 3}log pseudolikelihood = {res:-606.10467}  
Iteration 2:{space 3}log pseudolikelihood = {res:-599.25129}  
Iteration 3:{space 3}log pseudolikelihood = {res: -598.9177}  
Iteration 4:{space 3}log pseudolikelihood = {res:-598.91583}  
Iteration 5:{space 3}log pseudolikelihood = {res:-598.91583}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,576
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(15)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-598.91583{txt}{col 49}Pseudo R2{col 67}= {res}    0.4493

{txt}{ralign 80:(Std. Err. adjusted for {res:27} clusters in ccode)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}  prosanctions{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
loggedmigrants {c |}{col 16}{res}{space 2}-.3320088{col 28}{space 2} .1068593{col 39}{space 1}   -3.11{col 48}{space 3}0.002{col 56}{space 4}-.5414493{col 69}{space 3}-.1225684
{txt}{space 4}population {c |}{col 16}{res}{space 2} .7587729{col 28}{space 2} .3096765{col 39}{space 1}    2.45{col 48}{space 3}0.014{col 56}{space 4}  .151818{col 69}{space 3} 1.365728
{txt}{space 2}unemployment {c |}{col 16}{res}{space 2}-.1330392{col 28}{space 2} .0632876{col 39}{space 1}   -2.10{col 48}{space 3}0.036{col 56}{space 4}-.2570806{col 69}{space 3}-.0089978
{txt}{space 5}gdpgrowth {c |}{col 16}{res}{space 2} .0249187{col 28}{space 2} .1246798{col 39}{space 1}    0.20{col 48}{space 3}0.842{col 56}{space 4}-.2194492{col 69}{space 3} .2692866
{txt}{space 6}distance {c |}{col 16}{res}{space 2} .0007767{col 28}{space 2} .0003917{col 39}{space 1}    1.98{col 48}{space 3}0.047{col 56}{space 4} 8.93e-06{col 69}{space 3} .0015446
{txt}{space 11}col {c |}{col 16}{res}{space 2} 1.283182{col 28}{space 2} .4916813{col 39}{space 1}    2.61{col 48}{space 3}0.009{col 56}{space 4} .3195046{col 69}{space 3}  2.24686
{txt}{space 7}lnremit {c |}{col 16}{res}{space 2}-.2782657{col 28}{space 2} .1944346{col 39}{space 1}   -1.43{col 48}{space 3}0.152{col 56}{space 4}-.6593506{col 69}{space 3} .1028192
{txt}{space 5}libyabill {c |}{col 16}{res}{space 2} 3.220543{col 28}{space 2} .4969588{col 39}{space 1}    6.48{col 48}{space 3}0.000{col 56}{space 4} 2.246521{col 69}{space 3} 4.194564
{txt}{space 6}iranbill {c |}{col 16}{res}{space 2} 3.413714{col 28}{space 2} .3033983{col 39}{space 1}   11.25{col 48}{space 3}0.000{col 56}{space 4} 2.819064{col 69}{space 3} 4.008364
{txt}{space 14} {c |}
{space 9}ccode {c |}
{space 10}205  {c |}{col 16}{res}{space 2} 42.53642{col 28}{space 2} 17.74165{col 39}{space 1}    2.40{col 48}{space 3}0.017{col 56}{space 4} 7.763426{col 69}{space 3} 77.30942
{txt}{space 10}210  {c |}{col 16}{res}{space 2} 36.26603{col 28}{space 2} 14.35617{col 39}{space 1}    2.53{col 48}{space 3}0.012{col 56}{space 4} 8.128454{col 69}{space 3} 64.40361
{txt}{space 10}211  {c |}{col 16}{res}{space 2} 38.32238{col 28}{space 2} 15.82966{col 39}{space 1}    2.42{col 48}{space 3}0.015{col 56}{space 4} 7.296824{col 69}{space 3} 69.34794
{txt}{space 10}212  {c |}{col 16}{res}{space 2} 44.53281{col 28}{space 2} 18.95067{col 39}{space 1}    2.35{col 48}{space 3}0.019{col 56}{space 4} 7.390173{col 69}{space 3} 81.67545
{txt}{space 10}220  {c |}{col 16}{res}{space 2}-1.766212{col 28}{space 2} .6716008{col 39}{space 1}   -2.63{col 48}{space 3}0.009{col 56}{space 4}-3.082526{col 69}{space 3}-.4498989
{txt}{space 10}230  {c |}{col 16}{res}{space 2} 13.50171{col 28}{space 2} 5.536706{col 39}{space 1}    2.44{col 48}{space 3}0.015{col 56}{space 4}  2.64997{col 69}{space 3} 24.35346
{txt}{space 10}235  {c |}{col 16}{res}{space 2} 38.06608{col 28}{space 2} 15.66517{col 39}{space 1}    2.43{col 48}{space 3}0.015{col 56}{space 4} 7.362919{col 69}{space 3} 68.76924
{txt}{space 10}255  {c |}{col 16}{res}{space 2} -12.4375{col 28}{space 2} 5.148502{col 39}{space 1}   -2.42{col 48}{space 3}0.016{col 56}{space 4}-22.52838{col 69}{space 3} -2.34662
{txt}{space 10}290  {c |}{col 16}{res}{space 2} 18.61031{col 28}{space 2} 7.957629{col 39}{space 1}    2.34{col 48}{space 3}0.019{col 56}{space 4} 3.013645{col 69}{space 3} 34.20698
{txt}{space 10}305  {c |}{col 16}{res}{space 2} 41.54025{col 28}{space 2} 16.95619{col 39}{space 1}    2.45{col 48}{space 3}0.014{col 56}{space 4} 8.306728{col 69}{space 3} 74.77376
{txt}{space 10}310  {c |}{col 16}{res}{space 2} 40.60876{col 28}{space 2} 16.78208{col 39}{space 1}    2.42{col 48}{space 3}0.016{col 56}{space 4} 7.716495{col 69}{space 3} 73.50102
{txt}{space 10}316  {c |}{col 16}{res}{space 2} 39.45109{col 28}{space 2} 16.11516{col 39}{space 1}    2.45{col 48}{space 3}0.014{col 56}{space 4} 7.865966{col 69}{space 3} 71.03622
{txt}{space 10}317  {c |}{col 16}{res}{space 2} 43.22174{col 28}{space 2} 18.15143{col 39}{space 1}    2.38{col 48}{space 3}0.017{col 56}{space 4} 7.645592{col 69}{space 3} 78.79789
{txt}{space 10}325  {c |}{col 16}{res}{space 2} 3.346263{col 28}{space 2}  1.39639{col 39}{space 1}    2.40{col 48}{space 3}0.017{col 56}{space 4} .6093883{col 69}{space 3} 6.083137
{txt}{space 10}338  {c |}{col 16}{res}{space 2} 45.47039{col 28}{space 2} 19.26503{col 39}{space 1}    2.36{col 48}{space 3}0.018{col 56}{space 4} 7.711638{col 69}{space 3} 83.22915
{txt}{space 10}349  {c |}{col 16}{res}{space 2} 44.49724{col 28}{space 2} 18.47128{col 39}{space 1}    2.41{col 48}{space 3}0.016{col 56}{space 4} 8.294198{col 69}{space 3} 80.70029
{txt}{space 10}350  {c |}{col 16}{res}{space 2} 41.44995{col 28}{space 2} 16.89457{col 39}{space 1}    2.45{col 48}{space 3}0.014{col 56}{space 4} 8.337196{col 69}{space 3}  74.5627
{txt}{space 10}352  {c |}{col 16}{res}{space 2} 49.13002{col 28}{space 2} 19.69551{col 39}{space 1}    2.49{col 48}{space 3}0.013{col 56}{space 4} 10.52753{col 69}{space 3} 87.73252
{txt}{space 10}355  {c |}{col 16}{res}{space 2} 41.77898{col 28}{space 2} 17.48472{col 39}{space 1}    2.39{col 48}{space 3}0.017{col 56}{space 4} 7.509548{col 69}{space 3}  76.0484
{txt}{space 10}360  {c |}{col 16}{res}{space 2} 33.38125{col 28}{space 2} 13.58628{col 39}{space 1}    2.46{col 48}{space 3}0.014{col 56}{space 4} 6.752626{col 69}{space 3} 60.00987
{txt}{space 10}366  {c |}{col 16}{res}{space 2} 43.84623{col 28}{space 2} 18.56533{col 39}{space 1}    2.36{col 48}{space 3}0.018{col 56}{space 4} 7.458846{col 69}{space 3} 80.23361
{txt}{space 10}367  {c |}{col 16}{res}{space 2} 45.50389{col 28}{space 2} 18.77612{col 39}{space 1}    2.42{col 48}{space 3}0.015{col 56}{space 4} 8.703376{col 69}{space 3} 82.30441
{txt}{space 10}368  {c |}{col 16}{res}{space 2} 43.79849{col 28}{space 2} 18.60876{col 39}{space 1}    2.35{col 48}{space 3}0.019{col 56}{space 4} 7.325997{col 69}{space 3} 80.27098
{txt}{space 10}375  {c |}{col 16}{res}{space 2} 43.97356{col 28}{space 2} 17.75646{col 39}{space 1}    2.48{col 48}{space 3}0.013{col 56}{space 4}  9.17154{col 69}{space 3} 78.77559
{txt}{space 10}380  {c |}{col 16}{res}{space 2} 41.01965{col 28}{space 2} 16.94144{col 39}{space 1}    2.42{col 48}{space 3}0.015{col 56}{space 4} 7.815045{col 69}{space 3} 74.22426
{txt}{space 10}390  {c |}{col 16}{res}{space 2} 43.50792{col 28}{space 2} 17.75945{col 39}{space 1}    2.45{col 48}{space 3}0.014{col 56}{space 4} 8.700035{col 69}{space 3}  78.3158
{txt}{space 14} {c |}
{space 9}party {c |}
{space 10}EPP  {c |}{col 16}{res}{space 2} 1.863859{col 28}{space 2} 1.023988{col 39}{space 1}    1.82{col 48}{space 3}0.069{col 56}{space 4}  -.14312{col 69}{space 3} 3.870838
{txt}{space 10}S&D  {c |}{col 16}{res}{space 2} 1.898342{col 28}{space 2} 1.037115{col 39}{space 1}    1.83{col 48}{space 3}0.067{col 56}{space 4}-.1343654{col 69}{space 3}  3.93105
{txt}{space 3}Greens/EFA  {c |}{col 16}{res}{space 2}-.9422344{col 28}{space 2} .9887585{col 39}{space 1}   -0.95{col 48}{space 3}0.341{col 56}{space 4}-2.880166{col 69}{space 3} .9956968
{txt}{space 4}ALDE/ADLE  {c |}{col 16}{res}{space 2} 2.029044{col 28}{space 2} 1.049154{col 39}{space 1}    1.93{col 48}{space 3}0.053{col 56}{space 4}-.0272608{col 69}{space 3} 4.085348
{txt}{space 10}ECR  {c |}{col 16}{res}{space 2} 3.726655{col 28}{space 2} 1.027119{col 39}{space 1}    3.63{col 48}{space 3}0.000{col 56}{space 4}  1.71354{col 69}{space 3} 5.739771
{txt}{space 9}EFDD  {c |}{col 16}{res}{space 2}-.2524234{col 28}{space 2} .9640174{col 39}{space 1}   -0.26{col 48}{space 3}0.793{col 56}{space 4}-2.141863{col 69}{space 3} 1.637016
{txt}{space 6}GUE-NGL  {c |}{col 16}{res}{space 2}-.9296742{col 28}{space 2} 1.302255{col 39}{space 1}   -0.71{col 48}{space 3}0.475{col 56}{space 4}-3.482048{col 69}{space 3} 1.622699
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2}-49.39447{col 28}{space 2} 19.07603{col 39}{space 1}   -2.59{col 48}{space 3}0.010{col 56}{space 4}-86.78279{col 69}{space 3}-12.00614
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA6, title((A6))
{txt}
{com}. 
. estout MODELA1 MODELA2 MODELA3 MODELA4 MODELA5 MODELA6, cells(b(star fmt(3)) se(par fmt(3))) starlevels($^{c -(}+{c )-}$ 0.10 $^{c -(}*{c )-}$ 0.05 $^{c -(}**{c )-}$ 0.01 $^{c -(}***{c )-}$ 0.001) stats(N N_g r2, fmt(0 0 3) label(Observations Countries R$^2$)) label,  using "modelsa1_a6.tex", replace style(tex)
{res}{txt}(note: file modelsa1_a6.tex not found)
(output written to {browse  `"modelsa1_a6.tex"'})

{com}. 
. **FIGURE 2
. coefplot MODELA1, bylabel(Model A1) ///
> || MODELA2, bylabel(Model A2) ///
> || MODELA3, bylabel(Model A3) ///
> || MODELA4, bylabel(Model A4) ///
> || MODELA5, bylabel(Model A5) ///
> || MODELA6, bylabel(Model A6) keep(loggedmigrants) nolabel xsize(10) ysize(9) xline(0) graphregion(fcolor(white) lcolor(white))
{res}{txt}
{com}. 
. **TABLE A3
. //Model A7
. logit prosanctions loggedmigrants libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-640.84466}  
Iteration 2:{space 3}log pseudolikelihood = {res:-633.30402}  
Iteration 3:{space 3}log pseudolikelihood = {res:-632.92503}  
Iteration 4:{space 3}log pseudolikelihood = {res:-632.92081}  
Iteration 5:{space 3}log pseudolikelihood = {res:-632.92081}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(9)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-632.92081{txt}{col 49}Pseudo R2{col 67}= {res}    0.4601

{txt}{ralign 80:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}  prosanctions{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
loggedmigrants {c |}{col 16}{res}{space 2}-.1378095{col 28}{space 2} .0588368{col 39}{space 1}   -2.34{col 48}{space 3}0.019{col 56}{space 4}-.2531276{col 69}{space 3}-.0224914
{txt}{space 5}libyabill {c |}{col 16}{res}{space 2} 3.605257{col 28}{space 2} .3165535{col 39}{space 1}   11.39{col 48}{space 3}0.000{col 56}{space 4} 2.984824{col 69}{space 3} 4.225691
{txt}{space 6}iranbill {c |}{col 16}{res}{space 2} 3.926381{col 28}{space 2} .3953766{col 39}{space 1}    9.93{col 48}{space 3}0.000{col 56}{space 4} 3.151457{col 69}{space 3} 4.701305
{txt}{space 14} {c |}
{space 9}ccode {c |}
{space 10}205  {c |}{col 16}{res}{space 2}-1.315565{col 28}{space 2} .2463079{col 39}{space 1}   -5.34{col 48}{space 3}0.000{col 56}{space 4} -1.79832{col 69}{space 3}-.8328103
{txt}{space 10}210  {c |}{col 16}{res}{space 2} .7457135{col 28}{space 2} .2075794{col 39}{space 1}    3.59{col 48}{space 3}0.000{col 56}{space 4} .3388654{col 69}{space 3} 1.152562
{txt}{space 10}211  {c |}{col 16}{res}{space 2}-1.077377{col 28}{space 2} .3004121{col 39}{space 1}   -3.59{col 48}{space 3}0.000{col 56}{space 4}-1.666174{col 69}{space 3}-.4885803
{txt}{space 10}212  {c |}{col 16}{res}{space 2}-1.128633{col 28}{space 2} .4861851{col 39}{space 1}   -2.32{col 48}{space 3}0.020{col 56}{space 4}-2.081539{col 69}{space 3}-.1757283
{txt}{space 10}220  {c |}{col 16}{res}{space 2}-.8602942{col 28}{space 2} .2009167{col 39}{space 1}   -4.28{col 48}{space 3}0.000{col 56}{space 4}-1.254084{col 69}{space 3}-.4665047
{txt}{space 10}230  {c |}{col 16}{res}{space 2}-.9835756{col 28}{space 2}  .345286{col 39}{space 1}   -2.85{col 48}{space 3}0.004{col 56}{space 4}-1.660324{col 69}{space 3}-.3068276
{txt}{space 10}235  {c |}{col 16}{res}{space 2}-.4820532{col 28}{space 2} .4108319{col 39}{space 1}   -1.17{col 48}{space 3}0.241{col 56}{space 4}-1.287269{col 69}{space 3} .3231625
{txt}{space 10}255  {c |}{col 16}{res}{space 2}-.0817069{col 28}{space 2} .0958741{col 39}{space 1}   -0.85{col 48}{space 3}0.394{col 56}{space 4}-.2696166{col 69}{space 3} .1062029
{txt}{space 10}290  {c |}{col 16}{res}{space 2}-.6815966{col 28}{space 2} .3003638{col 39}{space 1}   -2.27{col 48}{space 3}0.023{col 56}{space 4}-1.270299{col 69}{space 3}-.0928944
{txt}{space 10}305  {c |}{col 16}{res}{space 2} -.493214{col 28}{space 2} .3122449{col 39}{space 1}   -1.58{col 48}{space 3}0.114{col 56}{space 4}-1.105203{col 69}{space 3} .1187748
{txt}{space 10}310  {c |}{col 16}{res}{space 2}-.6613846{col 28}{space 2} .2425386{col 39}{space 1}   -2.73{col 48}{space 3}0.006{col 56}{space 4}-1.136751{col 69}{space 3}-.1860177
{txt}{space 10}316  {c |}{col 16}{res}{space 2}-.1322699{col 28}{space 2} .3202442{col 39}{space 1}   -0.41{col 48}{space 3}0.680{col 56}{space 4}-.7599369{col 69}{space 3} .4953972
{txt}{space 10}317  {c |}{col 16}{res}{space 2}-1.086068{col 28}{space 2} .3710223{col 39}{space 1}   -2.93{col 48}{space 3}0.003{col 56}{space 4}-1.813258{col 69}{space 3}-.3588771
{txt}{space 10}325  {c |}{col 16}{res}{space 2}-.6173701{col 28}{space 2} .1390566{col 39}{space 1}   -4.44{col 48}{space 3}0.000{col 56}{space 4}-.8899161{col 69}{space 3} -.344824
{txt}{space 10}338  {c |}{col 16}{res}{space 2}-.5925894{col 28}{space 2} .2573762{col 39}{space 1}   -2.30{col 48}{space 3}0.021{col 56}{space 4}-1.097038{col 69}{space 3}-.0881412
{txt}{space 10}344  {c |}{col 16}{res}{space 2}-.2799893{col 28}{space 2} .5843872{col 39}{space 1}   -0.48{col 48}{space 3}0.632{col 56}{space 4}-1.425367{col 69}{space 3} .8653886
{txt}{space 10}349  {c |}{col 16}{res}{space 2}-.9822972{col 28}{space 2} .3686441{col 39}{space 1}   -2.66{col 48}{space 3}0.008{col 56}{space 4}-1.704826{col 69}{space 3}-.2597681
{txt}{space 10}350  {c |}{col 16}{res}{space 2}-1.572971{col 28}{space 2}  .245407{col 39}{space 1}   -6.41{col 48}{space 3}0.000{col 56}{space 4} -2.05396{col 69}{space 3}-1.091982
{txt}{space 10}352  {c |}{col 16}{res}{space 2}-.4500597{col 28}{space 2} .3178195{col 39}{space 1}   -1.42{col 48}{space 3}0.157{col 56}{space 4}-1.072974{col 69}{space 3}  .172855
{txt}{space 10}355  {c |}{col 16}{res}{space 2}-.9272651{col 28}{space 2}  .242147{col 39}{space 1}   -3.83{col 48}{space 3}0.000{col 56}{space 4}-1.401865{col 69}{space 3}-.4526656
{txt}{space 10}360  {c |}{col 16}{res}{space 2}-.4055224{col 28}{space 2} .1740712{col 39}{space 1}   -2.33{col 48}{space 3}0.020{col 56}{space 4}-.7466956{col 69}{space 3}-.0643492
{txt}{space 10}366  {c |}{col 16}{res}{space 2}-1.658827{col 28}{space 2} .5628448{col 39}{space 1}   -2.95{col 48}{space 3}0.003{col 56}{space 4}-2.761982{col 69}{space 3}-.5556712
{txt}{space 10}367  {c |}{col 16}{res}{space 2}-.9112233{col 28}{space 2} .4661624{col 39}{space 1}   -1.95{col 48}{space 3}0.051{col 56}{space 4}-1.824885{col 69}{space 3} .0024382
{txt}{space 10}368  {c |}{col 16}{res}{space 2}-1.084109{col 28}{space 2} .5106011{col 39}{space 1}   -2.12{col 48}{space 3}0.034{col 56}{space 4}-2.084869{col 69}{space 3}-.0833496
{txt}{space 10}375  {c |}{col 16}{res}{space 2} .4552819{col 28}{space 2} .2630964{col 39}{space 1}    1.73{col 48}{space 3}0.084{col 56}{space 4}-.0603776{col 69}{space 3} .9709414
{txt}{space 10}380  {c |}{col 16}{res}{space 2}-.6358924{col 28}{space 2} .0981771{col 39}{space 1}   -6.48{col 48}{space 3}0.000{col 56}{space 4}-.8283159{col 69}{space 3}-.4434689
{txt}{space 10}390  {c |}{col 16}{res}{space 2}-.3710878{col 28}{space 2} .1785623{col 39}{space 1}   -2.08{col 48}{space 3}0.038{col 56}{space 4}-.7210635{col 69}{space 3} -.021112
{txt}{space 14} {c |}
{space 9}party {c |}
{space 10}EPP  {c |}{col 16}{res}{space 2} 2.256738{col 28}{space 2} .9622463{col 39}{space 1}    2.35{col 48}{space 3}0.019{col 56}{space 4} .3707699{col 69}{space 3} 4.142706
{txt}{space 10}S&D  {c |}{col 16}{res}{space 2}  2.24755{col 28}{space 2} .9772144{col 39}{space 1}    2.30{col 48}{space 3}0.021{col 56}{space 4} .3322447{col 69}{space 3} 4.162855
{txt}{space 3}Greens/EFA  {c |}{col 16}{res}{space 2}-.7322755{col 28}{space 2} .9645771{col 39}{space 1}   -0.76{col 48}{space 3}0.448{col 56}{space 4}-2.622812{col 69}{space 3} 1.158261
{txt}{space 4}ALDE/ADLE  {c |}{col 16}{res}{space 2} 2.353044{col 28}{space 2} 1.008389{col 39}{space 1}    2.33{col 48}{space 3}0.020{col 56}{space 4} .3766377{col 69}{space 3}  4.32945
{txt}{space 10}ECR  {c |}{col 16}{res}{space 2} 4.148581{col 28}{space 2} 1.015399{col 39}{space 1}    4.09{col 48}{space 3}0.000{col 56}{space 4} 2.158435{col 69}{space 3} 6.138727
{txt}{space 9}EFDD  {c |}{col 16}{res}{space 2} .2443284{col 28}{space 2} .9543523{col 39}{space 1}    0.26{col 48}{space 3}0.798{col 56}{space 4}-1.626168{col 69}{space 3} 2.114824
{txt}{space 6}GUE-NGL  {c |}{col 16}{res}{space 2}-.9679572{col 28}{space 2} 1.258304{col 39}{space 1}   -0.77{col 48}{space 3}0.442{col 56}{space 4}-3.434187{col 69}{space 3} 1.498273
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2}-2.343782{col 28}{space 2} 1.200916{col 39}{space 1}   -1.95{col 48}{space 3}0.051{col 56}{space 4}-4.697535{col 69}{space 3} .0099703
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA7, title((A7))
{txt}
{com}. //Model A8
. logit prosanctions loggedmigrants population unemployment gdpgrowth distance col libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-635.53068}  
Iteration 2:{space 3}log pseudolikelihood = {res:-627.45257}  
Iteration 3:{space 3}log pseudolikelihood = {res: -627.0376}  
Iteration 4:{space 3}log pseudolikelihood = {res:-627.03436}  
Iteration 5:{space 3}log pseudolikelihood = {res:-627.03436}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(14)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-627.03436{txt}{col 49}Pseudo R2{col 67}= {res}    0.4651

{txt}{ralign 80:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}  prosanctions{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
loggedmigrants {c |}{col 16}{res}{space 2}-.2281655{col 28}{space 2}   .08724{col 39}{space 1}   -2.62{col 48}{space 3}0.009{col 56}{space 4}-.3991528{col 69}{space 3}-.0571782
{txt}{space 4}population {c |}{col 16}{res}{space 2} .3583438{col 28}{space 2} .2371057{col 39}{space 1}    1.51{col 48}{space 3}0.131{col 56}{space 4}-.1063747{col 69}{space 3} .8230624
{txt}{space 2}unemployment {c |}{col 16}{res}{space 2}-.0998719{col 28}{space 2} .0583547{col 39}{space 1}   -1.71{col 48}{space 3}0.087{col 56}{space 4} -.214245{col 69}{space 3} .0145012
{txt}{space 5}gdpgrowth {c |}{col 16}{res}{space 2} .0962875{col 28}{space 2} .1098631{col 39}{space 1}    0.88{col 48}{space 3}0.381{col 56}{space 4}-.1190402{col 69}{space 3} .3116151
{txt}{space 6}distance {c |}{col 16}{res}{space 2} .0005691{col 28}{space 2}  .000285{col 39}{space 1}    2.00{col 48}{space 3}0.046{col 56}{space 4} .0000104{col 69}{space 3} .0011278
{txt}{space 11}col {c |}{col 16}{res}{space 2} .8387646{col 28}{space 2} .3106687{col 39}{space 1}    2.70{col 48}{space 3}0.007{col 56}{space 4} .2298651{col 69}{space 3} 1.447664
{txt}{space 5}libyabill {c |}{col 16}{res}{space 2} 3.831456{col 28}{space 2} .3439807{col 39}{space 1}   11.14{col 48}{space 3}0.000{col 56}{space 4} 3.157266{col 69}{space 3} 4.505646
{txt}{space 6}iranbill {c |}{col 16}{res}{space 2} 3.744422{col 28}{space 2}  .376194{col 39}{space 1}    9.95{col 48}{space 3}0.000{col 56}{space 4} 3.007096{col 69}{space 3} 4.481749
{txt}{space 14} {c |}
{space 9}ccode {c |}
{space 10}205  {c |}{col 16}{res}{space 2} 19.85469{col 28}{space 2} 13.97587{col 39}{space 1}    1.42{col 48}{space 3}0.155{col 56}{space 4}-7.537504{col 69}{space 3} 47.24689
{txt}{space 10}210  {c |}{col 16}{res}{space 2} 17.74489{col 28}{space 2} 11.01035{col 39}{space 1}    1.61{col 48}{space 3}0.107{col 56}{space 4} -3.83499{col 69}{space 3} 39.32477
{txt}{space 10}211  {c |}{col 16}{res}{space 2} 17.76773{col 28}{space 2} 12.10695{col 39}{space 1}    1.47{col 48}{space 3}0.142{col 56}{space 4}-5.961456{col 69}{space 3} 41.49692
{txt}{space 10}212  {c |}{col 16}{res}{space 2} 20.68943{col 28}{space 2} 14.58168{col 39}{space 1}    1.42{col 48}{space 3}0.156{col 56}{space 4}-7.890134{col 69}{space 3} 49.26899
{txt}{space 10}220  {c |}{col 16}{res}{space 2}-1.173616{col 28}{space 2} .5713035{col 39}{space 1}   -2.05{col 48}{space 3}0.040{col 56}{space 4} -2.29335{col 69}{space 3} -.053882
{txt}{space 10}230  {c |}{col 16}{res}{space 2} 6.771872{col 28}{space 2} 4.278919{col 39}{space 1}    1.58{col 48}{space 3}0.114{col 56}{space 4}-1.614655{col 69}{space 3}  15.1584
{txt}{space 10}235  {c |}{col 16}{res}{space 2} 18.64486{col 28}{space 2} 12.26001{col 39}{space 1}    1.52{col 48}{space 3}0.128{col 56}{space 4}-5.384316{col 69}{space 3} 42.67404
{txt}{space 10}255  {c |}{col 16}{res}{space 2}-6.045986{col 28}{space 2} 4.098657{col 39}{space 1}   -1.48{col 48}{space 3}0.140{col 56}{space 4}-14.07921{col 69}{space 3} 1.987235
{txt}{space 10}290  {c |}{col 16}{res}{space 2} 8.901077{col 28}{space 2} 6.092125{col 39}{space 1}    1.46{col 48}{space 3}0.144{col 56}{space 4}-3.039269{col 69}{space 3} 20.84142
{txt}{space 10}305  {c |}{col 16}{res}{space 2} 19.70185{col 28}{space 2} 12.98973{col 39}{space 1}    1.52{col 48}{space 3}0.129{col 56}{space 4}-5.757559{col 69}{space 3} 45.16126
{txt}{space 10}310  {c |}{col 16}{res}{space 2}  19.3605{col 28}{space 2} 12.86073{col 39}{space 1}    1.51{col 48}{space 3}0.132{col 56}{space 4}-5.846071{col 69}{space 3} 44.56707
{txt}{space 10}316  {c |}{col 16}{res}{space 2} 19.21734{col 28}{space 2} 12.37184{col 39}{space 1}    1.55{col 48}{space 3}0.120{col 56}{space 4}-5.031021{col 69}{space 3}  43.4657
{txt}{space 10}317  {c |}{col 16}{res}{space 2} 20.64091{col 28}{space 2} 13.92346{col 39}{space 1}    1.48{col 48}{space 3}0.138{col 56}{space 4}-6.648565{col 69}{space 3} 47.93038
{txt}{space 10}325  {c |}{col 16}{res}{space 2} 1.721147{col 28}{space 2} 1.026506{col 39}{space 1}    1.68{col 48}{space 3}0.094{col 56}{space 4}-.2907673{col 69}{space 3} 3.733061
{txt}{space 10}338  {c |}{col 16}{res}{space 2}  22.6682{col 28}{space 2} 15.09781{col 39}{space 1}    1.50{col 48}{space 3}0.133{col 56}{space 4}-6.922973{col 69}{space 3} 52.25937
{txt}{space 10}344  {c |}{col 16}{res}{space 2} 22.29149{col 28}{space 2} 13.84198{col 39}{space 1}    1.61{col 48}{space 3}0.107{col 56}{space 4}-4.838288{col 69}{space 3} 49.42127
{txt}{space 10}349  {c |}{col 16}{res}{space 2} 21.56275{col 28}{space 2} 14.36208{col 39}{space 1}    1.50{col 48}{space 3}0.133{col 56}{space 4}-6.586399{col 69}{space 3}  49.7119
{txt}{space 10}350  {c |}{col 16}{res}{space 2} 20.24999{col 28}{space 2} 12.92582{col 39}{space 1}    1.57{col 48}{space 3}0.117{col 56}{space 4} -5.08415{col 69}{space 3} 45.58414
{txt}{space 10}352  {c |}{col 16}{res}{space 2} 24.01404{col 28}{space 2} 14.86952{col 39}{space 1}    1.61{col 48}{space 3}0.106{col 56}{space 4}-5.129691{col 69}{space 3} 53.15776
{txt}{space 10}355  {c |}{col 16}{res}{space 2} 20.54922{col 28}{space 2} 13.44023{col 39}{space 1}    1.53{col 48}{space 3}0.126{col 56}{space 4}-5.793143{col 69}{space 3} 46.89159
{txt}{space 10}360  {c |}{col 16}{res}{space 2} 16.14205{col 28}{space 2} 10.30613{col 39}{space 1}    1.57{col 48}{space 3}0.117{col 56}{space 4}-4.057586{col 69}{space 3} 36.34169
{txt}{space 10}366  {c |}{col 16}{res}{space 2} 20.43306{col 28}{space 2} 14.25068{col 39}{space 1}    1.43{col 48}{space 3}0.152{col 56}{space 4}-7.497759{col 69}{space 3} 48.36388
{txt}{space 10}367  {c |}{col 16}{res}{space 2} 21.60576{col 28}{space 2} 14.42068{col 39}{space 1}    1.50{col 48}{space 3}0.134{col 56}{space 4}-6.658254{col 69}{space 3} 49.86977
{txt}{space 10}368  {c |}{col 16}{res}{space 2} 21.11833{col 28}{space 2} 14.28717{col 39}{space 1}    1.48{col 48}{space 3}0.139{col 56}{space 4}-6.884005{col 69}{space 3} 49.12067
{txt}{space 10}375  {c |}{col 16}{res}{space 2} 21.47683{col 28}{space 2} 13.65535{col 39}{space 1}    1.57{col 48}{space 3}0.116{col 56}{space 4}-5.287167{col 69}{space 3} 48.24082
{txt}{space 10}380  {c |}{col 16}{res}{space 2} 18.97198{col 28}{space 2} 12.88755{col 39}{space 1}    1.47{col 48}{space 3}0.141{col 56}{space 4}-6.287153{col 69}{space 3} 44.23112
{txt}{space 10}390  {c |}{col 16}{res}{space 2} 20.63594{col 28}{space 2} 13.56436{col 39}{space 1}    1.52{col 48}{space 3}0.128{col 56}{space 4}-5.949713{col 69}{space 3} 47.22159
{txt}{space 14} {c |}
{space 9}party {c |}
{space 10}EPP  {c |}{col 16}{res}{space 2} 2.186067{col 28}{space 2} .9377774{col 39}{space 1}    2.33{col 48}{space 3}0.020{col 56}{space 4} .3480572{col 69}{space 3} 4.024077
{txt}{space 10}S&D  {c |}{col 16}{res}{space 2} 2.182074{col 28}{space 2} .9535556{col 39}{space 1}    2.29{col 48}{space 3}0.022{col 56}{space 4}  .313139{col 69}{space 3} 4.051008
{txt}{space 3}Greens/EFA  {c |}{col 16}{res}{space 2}-.8056865{col 28}{space 2} .9405854{col 39}{space 1}   -0.86{col 48}{space 3}0.392{col 56}{space 4}  -2.6492{col 69}{space 3} 1.037827
{txt}{space 4}ALDE/ADLE  {c |}{col 16}{res}{space 2} 2.329472{col 28}{space 2} .9917737{col 39}{space 1}    2.35{col 48}{space 3}0.019{col 56}{space 4} .3856307{col 69}{space 3} 4.273312
{txt}{space 10}ECR  {c |}{col 16}{res}{space 2} 3.993733{col 28}{space 2} .9879399{col 39}{space 1}    4.04{col 48}{space 3}0.000{col 56}{space 4} 2.057406{col 69}{space 3} 5.930059
{txt}{space 9}EFDD  {c |}{col 16}{res}{space 2} .0919102{col 28}{space 2} .9021175{col 39}{space 1}    0.10{col 48}{space 3}0.919{col 56}{space 4}-1.676208{col 69}{space 3} 1.860028
{txt}{space 6}GUE-NGL  {c |}{col 16}{res}{space 2}-.9796773{col 28}{space 2} 1.213121{col 39}{space 1}   -0.81{col 48}{space 3}0.419{col 56}{space 4} -3.35735{col 69}{space 3} 1.397995
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2}-25.80527{col 28}{space 2} 14.81133{col 39}{space 1}   -1.74{col 48}{space 3}0.081{col 56}{space 4}-54.83495{col 69}{space 3} 3.224407
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA8, title((A8))
{txt}
{com}. //Model A9
. logit prosanctions loggedmigrants population unemployment gdpgrowth distance col immigrantpolicy libyabill iranbill i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-650.65142}  
Iteration 2:{space 3}log pseudolikelihood = {res:-644.42551}  
Iteration 3:{space 3}log pseudolikelihood = {res:-644.07434}  
Iteration 4:{space 3}log pseudolikelihood = {res:-644.07066}  
Iteration 5:{space 3}log pseudolikelihood = {res:-644.07066}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}Wald chi2({res}16{txt}){col 67}= {res}    726.29
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-644.07066{txt}{col 49}Pseudo R2{col 67}= {res}    0.4506

{txt}{ralign 81:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}   prosanctions{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}loggedmigrants {c |}{col 17}{res}{space 2}-.1086774{col 29}{space 2}  .033611{col 40}{space 1}   -3.23{col 49}{space 3}0.001{col 57}{space 4}-.1745538{col 70}{space 3} -.042801
{txt}{space 5}population {c |}{col 17}{res}{space 2}-.0006238{col 29}{space 2} .0037614{col 40}{space 1}   -0.17{col 49}{space 3}0.868{col 57}{space 4}-.0079959{col 70}{space 3} .0067484
{txt}{space 3}unemployment {c |}{col 17}{res}{space 2}-.0525385{col 29}{space 2} .0214941{col 40}{space 1}   -2.44{col 49}{space 3}0.015{col 57}{space 4}-.0946663{col 70}{space 3}-.0104108
{txt}{space 6}gdpgrowth {c |}{col 17}{res}{space 2} .0103021{col 29}{space 2} .0621562{col 40}{space 1}    0.17{col 49}{space 3}0.868{col 57}{space 4}-.1115219{col 70}{space 3}  .132126
{txt}{space 7}distance {c |}{col 17}{res}{space 2}  .000228{col 29}{space 2} .0001103{col 40}{space 1}    2.07{col 49}{space 3}0.039{col 57}{space 4} .0000119{col 70}{space 3} .0004442
{txt}{space 12}col {c |}{col 17}{res}{space 2} .1912057{col 29}{space 2} .1806148{col 40}{space 1}    1.06{col 49}{space 3}0.290{col 57}{space 4}-.1627928{col 70}{space 3} .5452041
{txt}immigrantpolicy {c |}{col 17}{res}{space 2} .1028389{col 29}{space 2} .2538877{col 40}{space 1}    0.41{col 49}{space 3}0.685{col 57}{space 4}-.3947718{col 70}{space 3} .6004497
{txt}{space 6}libyabill {c |}{col 17}{res}{space 2} 3.785875{col 29}{space 2} .3160528{col 40}{space 1}   11.98{col 49}{space 3}0.000{col 57}{space 4} 3.166423{col 70}{space 3} 4.405327
{txt}{space 7}iranbill {c |}{col 17}{res}{space 2} 3.726265{col 29}{space 2} .3894637{col 40}{space 1}    9.57{col 49}{space 3}0.000{col 57}{space 4}  2.96293{col 70}{space 3} 4.489599
{txt}{space 15} {c |}
{space 10}party {c |}
{space 11}EPP  {c |}{col 17}{res}{space 2} 2.252965{col 29}{space 2} .9350766{col 40}{space 1}    2.41{col 49}{space 3}0.016{col 57}{space 4} .4202489{col 70}{space 3} 4.085682
{txt}{space 11}S&D  {c |}{col 17}{res}{space 2} 2.279906{col 29}{space 2} .9484381{col 40}{space 1}    2.40{col 49}{space 3}0.016{col 57}{space 4} .4210011{col 70}{space 3}  4.13881
{txt}{space 4}Greens/EFA  {c |}{col 17}{res}{space 2}-.7600329{col 29}{space 2} .9205801{col 40}{space 1}   -0.83{col 49}{space 3}0.409{col 57}{space 4}-2.564337{col 70}{space 3} 1.044271
{txt}{space 5}ALDE/ADLE  {c |}{col 17}{res}{space 2} 2.376075{col 29}{space 2} .9472198{col 40}{space 1}    2.51{col 49}{space 3}0.012{col 57}{space 4} .5195584{col 70}{space 3} 4.232592
{txt}{space 11}ECR  {c |}{col 17}{res}{space 2}  4.19021{col 29}{space 2} 1.029809{col 40}{space 1}    4.07{col 49}{space 3}0.000{col 57}{space 4} 2.171821{col 70}{space 3} 6.208598
{txt}{space 10}EFDD  {c |}{col 17}{res}{space 2} .2955967{col 29}{space 2} .9339434{col 40}{space 1}    0.32{col 49}{space 3}0.752{col 57}{space 4}-1.534899{col 70}{space 3} 2.126092
{txt}{space 7}GUE-NGL  {c |}{col 17}{res}{space 2}-.7565702{col 29}{space 2} 1.132962{col 40}{space 1}   -0.67{col 49}{space 3}0.504{col 57}{space 4}-2.977134{col 70}{space 3} 1.463994
{txt}{space 15} {c |}
{space 10}_cons {c |}{col 17}{res}{space 2}-3.487457{col 29}{space 2} 1.164785{col 40}{space 1}   -2.99{col 49}{space 3}0.003{col 57}{space 4}-5.770393{col 70}{space 3}-1.204521
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA9, title((A9))
{txt}
{com}. //Model A10
. logit prosanctions loggedmigrants population unemployment gdpgrowth distance col RWPvoteshare libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-633.63195}  
Iteration 2:{space 3}log pseudolikelihood = {res:-625.77118}  
Iteration 3:{space 3}log pseudolikelihood = {res:-625.38244}  
Iteration 4:{space 3}log pseudolikelihood = {res:-625.37927}  
Iteration 5:{space 3}log pseudolikelihood = {res:-625.37926}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(15)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-625.37926{txt}{col 49}Pseudo R2{col 67}= {res}    0.4665

{txt}{ralign 80:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}  prosanctions{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
loggedmigrants {c |}{col 16}{res}{space 2} -.200254{col 28}{space 2} .0856765{col 39}{space 1}   -2.34{col 48}{space 3}0.019{col 56}{space 4}-.3681769{col 69}{space 3}-.0323312
{txt}{space 4}population {c |}{col 16}{res}{space 2} .2862043{col 28}{space 2} .2164474{col 39}{space 1}    1.32{col 48}{space 3}0.186{col 56}{space 4}-.1380248{col 69}{space 3} .7104334
{txt}{space 2}unemployment {c |}{col 16}{res}{space 2}-.0958443{col 28}{space 2} .0471231{col 39}{space 1}   -2.03{col 48}{space 3}0.042{col 56}{space 4}-.1882038{col 69}{space 3}-.0034847
{txt}{space 5}gdpgrowth {c |}{col 16}{res}{space 2} .0877947{col 28}{space 2} .0964068{col 39}{space 1}    0.91{col 48}{space 3}0.362{col 56}{space 4}-.1011592{col 69}{space 3} .2767486
{txt}{space 6}distance {c |}{col 16}{res}{space 2} .0005561{col 28}{space 2} .0002511{col 39}{space 1}    2.21{col 48}{space 3}0.027{col 56}{space 4} .0000639{col 69}{space 3} .0010483
{txt}{space 11}col {c |}{col 16}{res}{space 2} .4134858{col 28}{space 2} .4061067{col 39}{space 1}    1.02{col 48}{space 3}0.309{col 56}{space 4}-.3824687{col 69}{space 3}  1.20944
{txt}{space 2}RWPvoteshare {c |}{col 16}{res}{space 2}-.0687695{col 28}{space 2} .0442811{col 39}{space 1}   -1.55{col 48}{space 3}0.120{col 56}{space 4}-.1555588{col 69}{space 3} .0180198
{txt}{space 5}libyabill {c |}{col 16}{res}{space 2} 4.002187{col 28}{space 2} .4102352{col 39}{space 1}    9.76{col 48}{space 3}0.000{col 56}{space 4} 3.198141{col 69}{space 3} 4.806233
{txt}{space 6}iranbill {c |}{col 16}{res}{space 2} 3.763838{col 28}{space 2}  .377124{col 39}{space 1}    9.98{col 48}{space 3}0.000{col 56}{space 4} 3.024688{col 69}{space 3} 4.502987
{txt}{space 14} {c |}
{space 9}ccode {c |}
{space 10}205  {c |}{col 16}{res}{space 2} 15.66024{col 28}{space 2} 12.71583{col 39}{space 1}    1.23{col 48}{space 3}0.218{col 56}{space 4}-9.262322{col 69}{space 3} 40.58281
{txt}{space 10}210  {c |}{col 16}{res}{space 2} 15.22489{col 28}{space 2} 10.05241{col 39}{space 1}    1.51{col 48}{space 3}0.130{col 56}{space 4}-4.477477{col 69}{space 3} 34.92725
{txt}{space 10}211  {c |}{col 16}{res}{space 2} 14.52882{col 28}{space 2} 11.01361{col 39}{space 1}    1.32{col 48}{space 3}0.187{col 56}{space 4}-7.057454{col 69}{space 3} 36.11509
{txt}{space 10}212  {c |}{col 16}{res}{space 2} 16.39943{col 28}{space 2} 13.26363{col 39}{space 1}    1.24{col 48}{space 3}0.216{col 56}{space 4}-9.596802{col 69}{space 3} 42.39567
{txt}{space 10}220  {c |}{col 16}{res}{space 2}-.1294053{col 28}{space 2} .8437127{col 39}{space 1}   -0.15{col 48}{space 3}0.878{col 56}{space 4}-1.783052{col 69}{space 3} 1.524241
{txt}{space 10}230  {c |}{col 16}{res}{space 2} 5.573701{col 28}{space 2} 3.765393{col 39}{space 1}    1.48{col 48}{space 3}0.139{col 56}{space 4}-1.806334{col 69}{space 3} 12.95374
{txt}{space 10}235  {c |}{col 16}{res}{space 2} 14.95815{col 28}{space 2}  11.1476{col 39}{space 1}    1.34{col 48}{space 3}0.180{col 56}{space 4}-6.890741{col 69}{space 3} 36.80703
{txt}{space 10}255  {c |}{col 16}{res}{space 2}-4.589449{col 28}{space 2} 3.765678{col 39}{space 1}   -1.22{col 48}{space 3}0.223{col 56}{space 4}-11.97004{col 69}{space 3} 2.791143
{txt}{space 10}290  {c |}{col 16}{res}{space 2} 7.170641{col 28}{space 2} 5.479169{col 39}{space 1}    1.31{col 48}{space 3}0.191{col 56}{space 4}-3.568332{col 69}{space 3} 17.90961
{txt}{space 10}305  {c |}{col 16}{res}{space 2} 17.46971{col 28}{space 2} 11.85397{col 39}{space 1}    1.47{col 48}{space 3}0.141{col 56}{space 4}-5.763644{col 69}{space 3} 40.70307
{txt}{space 10}310  {c |}{col 16}{res}{space 2}  16.7947{col 28}{space 2}  11.6609{col 39}{space 1}    1.44{col 48}{space 3}0.150{col 56}{space 4}-6.060242{col 69}{space 3} 39.64963
{txt}{space 10}316  {c |}{col 16}{res}{space 2} 15.97486{col 28}{space 2} 11.25092{col 39}{space 1}    1.42{col 48}{space 3}0.156{col 56}{space 4}-6.076538{col 69}{space 3} 38.02626
{txt}{space 10}317  {c |}{col 16}{res}{space 2} 17.02056{col 28}{space 2} 12.55461{col 39}{space 1}    1.36{col 48}{space 3}0.175{col 56}{space 4}-7.586018{col 69}{space 3} 41.62714
{txt}{space 10}325  {c |}{col 16}{res}{space 2} 1.958155{col 28}{space 2} .9309406{col 39}{space 1}    2.10{col 48}{space 3}0.035{col 56}{space 4} .1335454{col 69}{space 3} 3.782765
{txt}{space 10}338  {c |}{col 16}{res}{space 2}  18.1943{col 28}{space 2} 13.69341{col 39}{space 1}    1.33{col 48}{space 3}0.184{col 56}{space 4}-8.644285{col 69}{space 3} 45.03289
{txt}{space 10}344  {c |}{col 16}{res}{space 2} 18.52387{col 28}{space 2} 12.49718{col 39}{space 1}    1.48{col 48}{space 3}0.138{col 56}{space 4} -5.97016{col 69}{space 3}  43.0179
{txt}{space 10}349  {c |}{col 16}{res}{space 2} 17.46468{col 28}{space 2}  13.0464{col 39}{space 1}    1.34{col 48}{space 3}0.181{col 56}{space 4}  -8.1058{col 69}{space 3} 43.03515
{txt}{space 10}350  {c |}{col 16}{res}{space 2}  17.1932{col 28}{space 2} 11.58663{col 39}{space 1}    1.48{col 48}{space 3}0.138{col 56}{space 4}-5.516171{col 69}{space 3} 39.90258
{txt}{space 10}352  {c |}{col 16}{res}{space 2} 19.49503{col 28}{space 2} 13.48133{col 39}{space 1}    1.45{col 48}{space 3}0.148{col 56}{space 4}-6.927895{col 69}{space 3} 45.91796
{txt}{space 10}355  {c |}{col 16}{res}{space 2} 17.28962{col 28}{space 2} 12.14771{col 39}{space 1}    1.42{col 48}{space 3}0.155{col 56}{space 4}-6.519448{col 69}{space 3} 41.09869
{txt}{space 10}360  {c |}{col 16}{res}{space 2} 13.20074{col 28}{space 2} 9.345599{col 39}{space 1}    1.41{col 48}{space 3}0.158{col 56}{space 4}-5.116294{col 69}{space 3} 31.51778
{txt}{space 10}366  {c |}{col 16}{res}{space 2} 16.17589{col 28}{space 2} 12.94476{col 39}{space 1}    1.25{col 48}{space 3}0.211{col 56}{space 4}-9.195378{col 69}{space 3} 41.54716
{txt}{space 10}367  {c |}{col 16}{res}{space 2} 18.28125{col 28}{space 2} 13.06169{col 39}{space 1}    1.40{col 48}{space 3}0.162{col 56}{space 4}-7.319183{col 69}{space 3} 43.88168
{txt}{space 10}368  {c |}{col 16}{res}{space 2} 16.92819{col 28}{space 2} 12.92927{col 39}{space 1}    1.31{col 48}{space 3}0.190{col 56}{space 4} -8.41272{col 69}{space 3} 42.26909
{txt}{space 10}375  {c |}{col 16}{res}{space 2} 18.05193{col 28}{space 2} 12.41265{col 39}{space 1}    1.45{col 48}{space 3}0.146{col 56}{space 4}-6.276416{col 69}{space 3} 42.38027
{txt}{space 10}380  {c |}{col 16}{res}{space 2} 15.63476{col 28}{space 2} 11.72188{col 39}{space 1}    1.33{col 48}{space 3}0.182{col 56}{space 4}-7.339705{col 69}{space 3} 38.60923
{txt}{space 10}390  {c |}{col 16}{res}{space 2} 17.38014{col 28}{space 2} 12.35059{col 39}{space 1}    1.41{col 48}{space 3}0.159{col 56}{space 4}-6.826571{col 69}{space 3} 41.58686
{txt}{space 14} {c |}
{space 9}party {c |}
{space 10}EPP  {c |}{col 16}{res}{space 2} 2.188199{col 28}{space 2} .9502001{col 39}{space 1}    2.30{col 48}{space 3}0.021{col 56}{space 4} .3258414{col 69}{space 3} 4.050557
{txt}{space 10}S&D  {c |}{col 16}{res}{space 2} 2.193849{col 28}{space 2} .9687305{col 39}{space 1}    2.26{col 48}{space 3}0.024{col 56}{space 4} .2951725{col 69}{space 3} 4.092526
{txt}{space 3}Greens/EFA  {c |}{col 16}{res}{space 2}-.7589676{col 28}{space 2} .9666594{col 39}{space 1}   -0.79{col 48}{space 3}0.432{col 56}{space 4}-2.653585{col 69}{space 3}  1.13565
{txt}{space 4}ALDE/ADLE  {c |}{col 16}{res}{space 2} 2.341007{col 28}{space 2} 1.002681{col 39}{space 1}    2.33{col 48}{space 3}0.020{col 56}{space 4} .3757887{col 69}{space 3} 4.306225
{txt}{space 10}ECR  {c |}{col 16}{res}{space 2}  4.00656{col 28}{space 2} .9941788{col 39}{space 1}    4.03{col 48}{space 3}0.000{col 56}{space 4} 2.058005{col 69}{space 3} 5.955114
{txt}{space 9}EFDD  {c |}{col 16}{res}{space 2} .1219093{col 28}{space 2} .9285558{col 39}{space 1}    0.13{col 48}{space 3}0.896{col 56}{space 4}-1.698026{col 69}{space 3} 1.941845
{txt}{space 6}GUE-NGL  {c |}{col 16}{res}{space 2}-.9389747{col 28}{space 2} 1.242029{col 39}{space 1}   -0.76{col 48}{space 3}0.450{col 56}{space 4}-3.373307{col 69}{space 3} 1.495357
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2}-21.53434{col 28}{space 2} 13.52237{col 39}{space 1}   -1.59{col 48}{space 3}0.111{col 56}{space 4}-48.03771{col 69}{space 3} 4.969022
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA10, title((A10))
{txt}
{com}. //Model A11
. logit prosanctions loggedmigrants population unemployment gdpgrowth distance col exportsGDP importsGDP libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1162.7147}  
Iteration 1:{space 3}log pseudolikelihood = {res:-628.85054}  
Iteration 2:{space 3}log pseudolikelihood = {res:-620.84329}  
Iteration 3:{space 3}log pseudolikelihood = {res:-620.44168}  
Iteration 4:{space 3}log pseudolikelihood = {res: -620.4383}  
Iteration 5:{space 3}log pseudolikelihood = {res: -620.4383}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,699
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(16)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res} -620.4383{txt}{col 49}Pseudo R2{col 67}= {res}    0.4664

{txt}{ralign 80:(Std. Err. adjusted for {res:27} clusters in ccode)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}  prosanctions{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
loggedmigrants {c |}{col 16}{res}{space 2}-.2307852{col 28}{space 2} .0994619{col 39}{space 1}   -2.32{col 48}{space 3}0.020{col 56}{space 4}-.4257269{col 69}{space 3}-.0358434
{txt}{space 4}population {c |}{col 16}{res}{space 2} .4136248{col 28}{space 2} .2293468{col 39}{space 1}    1.80{col 48}{space 3}0.071{col 56}{space 4}-.0358867{col 69}{space 3} .8631362
{txt}{space 2}unemployment {c |}{col 16}{res}{space 2} -.067963{col 28}{space 2} .0574159{col 39}{space 1}   -1.18{col 48}{space 3}0.237{col 56}{space 4}-.1804961{col 69}{space 3} .0445702
{txt}{space 5}gdpgrowth {c |}{col 16}{res}{space 2} .1271759{col 28}{space 2} .1132269{col 39}{space 1}    1.12{col 48}{space 3}0.261{col 56}{space 4}-.0947447{col 69}{space 3} .3490966
{txt}{space 6}distance {c |}{col 16}{res}{space 2} .0005407{col 28}{space 2} .0003433{col 39}{space 1}    1.57{col 48}{space 3}0.115{col 56}{space 4}-.0001322{col 69}{space 3} .0012136
{txt}{space 11}col {c |}{col 16}{res}{space 2} .8797333{col 28}{space 2} .3167133{col 39}{space 1}    2.78{col 48}{space 3}0.005{col 56}{space 4} .2589867{col 69}{space 3}  1.50048
{txt}{space 4}exportsGDP {c |}{col 16}{res}{space 2} 250.0449{col 28}{space 2} 202.5478{col 39}{space 1}    1.23{col 48}{space 3}0.217{col 56}{space 4}-146.9414{col 69}{space 3} 647.0312
{txt}{space 4}importsGDP {c |}{col 16}{res}{space 2}-834.3956{col 28}{space 2} 532.5244{col 39}{space 1}   -1.57{col 48}{space 3}0.117{col 56}{space 4}-1878.124{col 69}{space 3} 209.3331
{txt}{space 5}libyabill {c |}{col 16}{res}{space 2} 3.966187{col 28}{space 2} .3345556{col 39}{space 1}   11.86{col 48}{space 3}0.000{col 56}{space 4}  3.31047{col 69}{space 3} 4.621904
{txt}{space 6}iranbill {c |}{col 16}{res}{space 2} 3.618852{col 28}{space 2} .4169756{col 39}{space 1}    8.68{col 48}{space 3}0.000{col 56}{space 4} 2.801595{col 69}{space 3} 4.436109
{txt}{space 14} {c |}
{space 9}ccode {c |}
{space 10}205  {c |}{col 16}{res}{space 2} 23.32059{col 28}{space 2} 13.47297{col 39}{space 1}    1.73{col 48}{space 3}0.083{col 56}{space 4}-3.085939{col 69}{space 3} 49.72712
{txt}{space 10}210  {c |}{col 16}{res}{space 2} 20.70414{col 28}{space 2} 10.64953{col 39}{space 1}    1.94{col 48}{space 3}0.052{col 56}{space 4}-.1685539{col 69}{space 3} 41.57684
{txt}{space 10}211  {c |}{col 16}{res}{space 2} 20.64188{col 28}{space 2} 11.69072{col 39}{space 1}    1.77{col 48}{space 3}0.077{col 56}{space 4} -2.27151{col 69}{space 3} 43.55527
{txt}{space 10}220  {c |}{col 16}{res}{space 2}-1.219485{col 28}{space 2} .5687593{col 39}{space 1}   -2.14{col 48}{space 3}0.032{col 56}{space 4}-2.334233{col 69}{space 3}-.1047375
{txt}{space 10}230  {c |}{col 16}{res}{space 2} 7.349094{col 28}{space 2} 4.115325{col 39}{space 1}    1.79{col 48}{space 3}0.074{col 56}{space 4}-.7167953{col 69}{space 3} 15.41498
{txt}{space 10}235  {c |}{col 16}{res}{space 2} 21.39754{col 28}{space 2}  11.8315{col 39}{space 1}    1.81{col 48}{space 3}0.071{col 56}{space 4}-1.791781{col 69}{space 3} 44.58686
{txt}{space 10}255  {c |}{col 16}{res}{space 2}-6.862515{col 28}{space 2} 3.970767{col 39}{space 1}   -1.73{col 48}{space 3}0.084{col 56}{space 4}-14.64507{col 69}{space 3} .9200447
{txt}{space 10}290  {c |}{col 16}{res}{space 2} 10.15348{col 28}{space 2} 5.857394{col 39}{space 1}    1.73{col 48}{space 3}0.083{col 56}{space 4}-1.326802{col 69}{space 3} 21.63376
{txt}{space 10}305  {c |}{col 16}{res}{space 2} 23.22119{col 28}{space 2} 12.54153{col 39}{space 1}    1.85{col 48}{space 3}0.064{col 56}{space 4}-1.359763{col 69}{space 3} 47.80214
{txt}{space 10}310  {c |}{col 16}{res}{space 2} 22.14006{col 28}{space 2} 12.42873{col 39}{space 1}    1.78{col 48}{space 3}0.075{col 56}{space 4}-2.219806{col 69}{space 3} 46.49992
{txt}{space 10}316  {c |}{col 16}{res}{space 2} 22.15142{col 28}{space 2} 11.95211{col 39}{space 1}    1.85{col 48}{space 3}0.064{col 56}{space 4}-1.274278{col 69}{space 3} 45.57712
{txt}{space 10}317  {c |}{col 16}{res}{space 2} 23.57137{col 28}{space 2} 13.42658{col 39}{space 1}    1.76{col 48}{space 3}0.079{col 56}{space 4}-2.744244{col 69}{space 3} 49.88698
{txt}{space 10}325  {c |}{col 16}{res}{space 2} 2.069976{col 28}{space 2}  .976865{col 39}{space 1}    2.12{col 48}{space 3}0.034{col 56}{space 4} .1553557{col 69}{space 3} 3.984596
{txt}{space 10}338  {c |}{col 16}{res}{space 2} 25.31598{col 28}{space 2} 14.44908{col 39}{space 1}    1.75{col 48}{space 3}0.080{col 56}{space 4}-3.003701{col 69}{space 3} 53.63567
{txt}{space 10}344  {c |}{col 16}{res}{space 2} 25.58901{col 28}{space 2} 13.33225{col 39}{space 1}    1.92{col 48}{space 3}0.055{col 56}{space 4}-.5417303{col 69}{space 3} 51.71975
{txt}{space 10}349  {c |}{col 16}{res}{space 2} 24.74646{col 28}{space 2} 13.83974{col 39}{space 1}    1.79{col 48}{space 3}0.074{col 56}{space 4}-2.378926{col 69}{space 3} 51.87185
{txt}{space 10}350  {c |}{col 16}{res}{space 2} 23.35189{col 28}{space 2} 12.43013{col 39}{space 1}    1.88{col 48}{space 3}0.060{col 56}{space 4}-1.010719{col 69}{space 3}  47.7145
{txt}{space 10}352  {c |}{col 16}{res}{space 2} 27.36703{col 28}{space 2} 14.38638{col 39}{space 1}    1.90{col 48}{space 3}0.057{col 56}{space 4}-.8297492{col 69}{space 3} 55.56382
{txt}{space 10}355  {c |}{col 16}{res}{space 2} 23.55681{col 28}{space 2} 12.94391{col 39}{space 1}    1.82{col 48}{space 3}0.069{col 56}{space 4} -1.81278{col 69}{space 3}  48.9264
{txt}{space 10}360  {c |}{col 16}{res}{space 2} 18.37057{col 28}{space 2} 9.961924{col 39}{space 1}    1.84{col 48}{space 3}0.065{col 56}{space 4}-1.154439{col 69}{space 3} 37.89558
{txt}{space 10}366  {c |}{col 16}{res}{space 2}  23.7177{col 28}{space 2} 13.73456{col 39}{space 1}    1.73{col 48}{space 3}0.084{col 56}{space 4}-3.201538{col 69}{space 3} 50.63693
{txt}{space 10}367  {c |}{col 16}{res}{space 2}  24.4163{col 28}{space 2} 13.89439{col 39}{space 1}    1.76{col 48}{space 3}0.079{col 56}{space 4}-2.816206{col 69}{space 3} 51.64881
{txt}{space 10}368  {c |}{col 16}{res}{space 2} 23.86985{col 28}{space 2} 13.73932{col 39}{space 1}    1.74{col 48}{space 3}0.082{col 56}{space 4}-3.058721{col 69}{space 3} 50.79842
{txt}{space 10}375  {c |}{col 16}{res}{space 2} 24.61233{col 28}{space 2} 13.20408{col 39}{space 1}    1.86{col 48}{space 3}0.062{col 56}{space 4}-1.267195{col 69}{space 3} 50.49186
{txt}{space 10}380  {c |}{col 16}{res}{space 2} 21.88157{col 28}{space 2} 12.46376{col 39}{space 1}    1.76{col 48}{space 3}0.079{col 56}{space 4}-2.546943{col 69}{space 3} 46.31009
{txt}{space 10}390  {c |}{col 16}{res}{space 2} 23.86112{col 28}{space 2} 13.11462{col 39}{space 1}    1.82{col 48}{space 3}0.069{col 56}{space 4}-1.843054{col 69}{space 3}  49.5653
{txt}{space 14} {c |}
{space 9}party {c |}
{space 10}EPP  {c |}{col 16}{res}{space 2}  2.15542{col 28}{space 2}  .956392{col 39}{space 1}    2.25{col 48}{space 3}0.024{col 56}{space 4} .2809261{col 69}{space 3} 4.029914
{txt}{space 10}S&D  {c |}{col 16}{res}{space 2} 2.151624{col 28}{space 2} .9715365{col 39}{space 1}    2.21{col 48}{space 3}0.027{col 56}{space 4} .2474476{col 69}{space 3} 4.055801
{txt}{space 3}Greens/EFA  {c |}{col 16}{res}{space 2}-.8082117{col 28}{space 2} .9621616{col 39}{space 1}   -0.84{col 48}{space 3}0.401{col 56}{space 4}-2.694014{col 69}{space 3}  1.07759
{txt}{space 4}ALDE/ADLE  {c |}{col 16}{res}{space 2} 2.238578{col 28}{space 2} 1.003114{col 39}{space 1}    2.23{col 48}{space 3}0.026{col 56}{space 4} .2725103{col 69}{space 3} 4.204646
{txt}{space 10}ECR  {c |}{col 16}{res}{space 2}  3.98946{col 28}{space 2} .9876843{col 39}{space 1}    4.04{col 48}{space 3}0.000{col 56}{space 4} 2.053634{col 69}{space 3} 5.925285
{txt}{space 9}EFDD  {c |}{col 16}{res}{space 2} .0773578{col 28}{space 2} .9280476{col 39}{space 1}    0.08{col 48}{space 3}0.934{col 56}{space 4}-1.741582{col 69}{space 3} 1.896298
{txt}{space 6}GUE-NGL  {c |}{col 16}{res}{space 2}-.9721527{col 28}{space 2} 1.240663{col 39}{space 1}   -0.78{col 48}{space 3}0.433{col 56}{space 4}-3.403807{col 69}{space 3} 1.459502
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2}-29.45583{col 28}{space 2}  14.3683{col 39}{space 1}   -2.05{col 48}{space 3}0.040{col 56}{space 4}-57.61718{col 69}{space 3}-1.294473
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA11, title((A11))
{txt}
{com}. //Model A12
. logit prosanctions loggedmigrants population unemployment gdpgrowth distance col pincometax sstax libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1045.4561}  
Iteration 1:{space 3}log pseudolikelihood = {res: -593.1858}  
Iteration 2:{space 3}log pseudolikelihood = {res:-587.28805}  
Iteration 3:{space 3}log pseudolikelihood = {res:-586.93778}  
Iteration 4:{space 3}log pseudolikelihood = {res:-586.93549}  
Iteration 5:{space 3}log pseudolikelihood = {res:-586.93549}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,522
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(16)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-586.93549{txt}{col 49}Pseudo R2{col 67}= {res}    0.4386

{txt}{ralign 80:(Std. Err. adjusted for {res:22} clusters in ccode)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}  prosanctions{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
loggedmigrants {c |}{col 16}{res}{space 2}-.1937965{col 28}{space 2} .0889436{col 39}{space 1}   -2.18{col 48}{space 3}0.029{col 56}{space 4}-.3681227{col 69}{space 3}-.0194702
{txt}{space 4}population {c |}{col 16}{res}{space 2}  .246852{col 28}{space 2} .2108071{col 39}{space 1}    1.17{col 48}{space 3}0.242{col 56}{space 4}-.1663223{col 69}{space 3} .6600263
{txt}{space 2}unemployment {c |}{col 16}{res}{space 2}-.0271749{col 28}{space 2} .0567336{col 39}{space 1}   -0.48{col 48}{space 3}0.632{col 56}{space 4}-.1383707{col 69}{space 3} .0840209
{txt}{space 5}gdpgrowth {c |}{col 16}{res}{space 2}-.0226997{col 28}{space 2} .1279792{col 39}{space 1}   -0.18{col 48}{space 3}0.859{col 56}{space 4}-.2735343{col 69}{space 3} .2281349
{txt}{space 6}distance {c |}{col 16}{res}{space 2} .0003546{col 28}{space 2} .0003249{col 39}{space 1}    1.09{col 48}{space 3}0.275{col 56}{space 4}-.0002821{col 69}{space 3} .0009914
{txt}{space 11}col {c |}{col 16}{res}{space 2} .4509374{col 28}{space 2} .5870038{col 39}{space 1}    0.77{col 48}{space 3}0.442{col 56}{space 4}-.6995689{col 69}{space 3} 1.601444
{txt}{space 4}pincometax {c |}{col 16}{res}{space 2}-.3106582{col 28}{space 2} .2536434{col 39}{space 1}   -1.22{col 48}{space 3}0.221{col 56}{space 4}-.8077902{col 69}{space 3} .1864737
{txt}{space 9}sstax {c |}{col 16}{res}{space 2} .0008604{col 28}{space 2} .5905899{col 39}{space 1}    0.00{col 48}{space 3}0.999{col 56}{space 4}-1.156675{col 69}{space 3} 1.158395
{txt}{space 5}libyabill {c |}{col 16}{res}{space 2} 3.697413{col 28}{space 2} .4162451{col 39}{space 1}    8.88{col 48}{space 3}0.000{col 56}{space 4} 2.881588{col 69}{space 3} 4.513239
{txt}{space 6}iranbill {c |}{col 16}{res}{space 2} 3.668205{col 28}{space 2} .3949264{col 39}{space 1}    9.29{col 48}{space 3}0.000{col 56}{space 4} 2.894163{col 69}{space 3} 4.442247
{txt}{space 14} {c |}
{space 9}ccode {c |}
{space 10}205  {c |}{col 16}{res}{space 2} 13.09249{col 28}{space 2} 12.54509{col 39}{space 1}    1.04{col 48}{space 3}0.297{col 56}{space 4}-11.49543{col 69}{space 3}  37.6804
{txt}{space 10}210  {c |}{col 16}{res}{space 2} 11.79333{col 28}{space 2} 9.258872{col 39}{space 1}    1.27{col 48}{space 3}0.203{col 56}{space 4}-6.353729{col 69}{space 3} 29.94038
{txt}{space 10}211  {c |}{col 16}{res}{space 2} 12.91572{col 28}{space 2} 10.08344{col 39}{space 1}    1.28{col 48}{space 3}0.200{col 56}{space 4}-6.847455{col 69}{space 3} 32.67889
{txt}{space 10}212  {c |}{col 16}{res}{space 2} 14.04554{col 28}{space 2} 12.16885{col 39}{space 1}    1.15{col 48}{space 3}0.248{col 56}{space 4}-9.804973{col 69}{space 3} 37.89605
{txt}{space 10}220  {c |}{col 16}{res}{space 2}-1.531517{col 28}{space 2} 7.167579{col 39}{space 1}   -0.21{col 48}{space 3}0.831{col 56}{space 4}-15.57971{col 69}{space 3} 12.51668
{txt}{space 10}230  {c |}{col 16}{res}{space 2} 2.989359{col 28}{space 2} 4.135751{col 39}{space 1}    0.72{col 48}{space 3}0.470{col 56}{space 4}-5.116564{col 69}{space 3} 11.09528
{txt}{space 10}235  {c |}{col 16}{res}{space 2} 11.60053{col 28}{space 2}  10.5807{col 39}{space 1}    1.10{col 48}{space 3}0.273{col 56}{space 4}-9.137266{col 69}{space 3} 32.33833
{txt}{space 10}255  {c |}{col 16}{res}{space 2}-4.138333{col 28}{space 2} 6.750277{col 39}{space 1}   -0.61{col 48}{space 3}0.540{col 56}{space 4}-17.36863{col 69}{space 3} 9.091967
{txt}{space 10}290  {c |}{col 16}{res}{space 2} 4.479773{col 28}{space 2} 5.437526{col 39}{space 1}    0.82{col 48}{space 3}0.410{col 56}{space 4}-6.177582{col 69}{space 3} 15.13713
{txt}{space 10}305  {c |}{col 16}{res}{space 2} 13.67064{col 28}{space 2}  11.0267{col 39}{space 1}    1.24{col 48}{space 3}0.215{col 56}{space 4}-7.941294{col 69}{space 3} 35.28257
{txt}{space 10}310  {c |}{col 16}{res}{space 2} 12.01497{col 28}{space 2} 10.86085{col 39}{space 1}    1.11{col 48}{space 3}0.269{col 56}{space 4}-9.271918{col 69}{space 3} 33.30185
{txt}{space 10}316  {c |}{col 16}{res}{space 2} 11.39451{col 28}{space 2} 10.67656{col 39}{space 1}    1.07{col 48}{space 3}0.286{col 56}{space 4}-9.531162{col 69}{space 3} 32.32018
{txt}{space 10}317  {c |}{col 16}{res}{space 2} 11.80852{col 28}{space 2} 11.95599{col 39}{space 1}    0.99{col 48}{space 3}0.323{col 56}{space 4}-11.62479{col 69}{space 3} 35.24183
{txt}{space 10}325  {c |}{col 16}{res}{space 2} 1.431635{col 28}{space 2} 4.064396{col 39}{space 1}    0.35{col 48}{space 3}0.725{col 56}{space 4}-6.534434{col 69}{space 3} 9.397704
{txt}{space 10}349  {c |}{col 16}{res}{space 2} 13.24191{col 28}{space 2} 12.20379{col 39}{space 1}    1.09{col 48}{space 3}0.278{col 56}{space 4}-10.67707{col 69}{space 3}  37.1609
{txt}{space 10}350  {c |}{col 16}{res}{space 2} 11.31289{col 28}{space 2} 11.34254{col 39}{space 1}    1.00{col 48}{space 3}0.319{col 56}{space 4}-10.91809{col 69}{space 3} 33.54387
{txt}{space 10}366  {c |}{col 16}{res}{space 2} 12.34453{col 28}{space 2} 12.12383{col 39}{space 1}    1.02{col 48}{space 3}0.309{col 56}{space 4}-11.41773{col 69}{space 3} 36.10679
{txt}{space 10}367  {c |}{col 16}{res}{space 2}  13.3999{col 28}{space 2} 12.59648{col 39}{space 1}    1.06{col 48}{space 3}0.287{col 56}{space 4}-11.28875{col 69}{space 3} 38.08855
{txt}{space 10}375  {c |}{col 16}{res}{space 2} 15.85997{col 28}{space 2} 11.28932{col 39}{space 1}    1.40{col 48}{space 3}0.160{col 56}{space 4}-6.266698{col 69}{space 3} 37.98664
{txt}{space 10}380  {c |}{col 16}{res}{space 2} 13.82943{col 28}{space 2} 10.74619{col 39}{space 1}    1.29{col 48}{space 3}0.198{col 56}{space 4}-7.232718{col 69}{space 3} 34.89158
{txt}{space 10}390  {c |}{col 16}{res}{space 2} 19.25537{col 28}{space 2}  12.8203{col 39}{space 1}    1.50{col 48}{space 3}0.133{col 56}{space 4} -5.87195{col 69}{space 3} 44.38269
{txt}{space 14} {c |}
{space 9}party {c |}
{space 10}EPP  {c |}{col 16}{res}{space 2} 2.289784{col 28}{space 2}  1.00515{col 39}{space 1}    2.28{col 48}{space 3}0.023{col 56}{space 4} .3197257{col 69}{space 3} 4.259843
{txt}{space 10}S&D  {c |}{col 16}{res}{space 2} 2.284586{col 28}{space 2} 1.016766{col 39}{space 1}    2.25{col 48}{space 3}0.025{col 56}{space 4} .2917616{col 69}{space 3}  4.27741
{txt}{space 3}Greens/EFA  {c |}{col 16}{res}{space 2}-.5843287{col 28}{space 2} 1.012784{col 39}{space 1}   -0.58{col 48}{space 3}0.564{col 56}{space 4} -2.56935{col 69}{space 3} 1.400692
{txt}{space 4}ALDE/ADLE  {c |}{col 16}{res}{space 2} 2.478928{col 28}{space 2} 1.068824{col 39}{space 1}    2.32{col 48}{space 3}0.020{col 56}{space 4} .3840711{col 69}{space 3} 4.573785
{txt}{space 10}ECR  {c |}{col 16}{res}{space 2} 4.174321{col 28}{space 2} 1.032969{col 39}{space 1}    4.04{col 48}{space 3}0.000{col 56}{space 4}  2.14974{col 69}{space 3} 6.198902
{txt}{space 9}EFDD  {c |}{col 16}{res}{space 2} .1837562{col 28}{space 2} .9590512{col 39}{space 1}    0.19{col 48}{space 3}0.848{col 56}{space 4} -1.69595{col 69}{space 3} 2.063462
{txt}{space 6}GUE-NGL  {c |}{col 16}{res}{space 2} -.783653{col 28}{space 2} 1.301535{col 39}{space 1}   -0.60{col 48}{space 3}0.547{col 56}{space 4}-3.334616{col 69}{space 3}  1.76731
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2}-15.78843{col 28}{space 2} 15.79566{col 39}{space 1}   -1.00{col 48}{space 3}0.318{col 56}{space 4}-46.74735{col 69}{space 3} 15.17049
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA12, title((A12))
{txt}
{com}. //Model A13
. logit prosanctions loggedmigrants population unemployment gdpgrowth distance col logged_refugee libyabill iranbill i.ccode i.party, cluster(ccode)

{txt}note: 349.ccode != 0 predicts success perfectly
      349.ccode dropped and 6 obs not used

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1106.6407}  
Iteration 1:{space 3}log pseudolikelihood = {res:-594.94073}  
Iteration 2:{space 3}log pseudolikelihood = {res:-587.34885}  
Iteration 3:{space 3}log pseudolikelihood = {res:-586.98094}  
Iteration 4:{space 3}log pseudolikelihood = {res:-586.97792}  
Iteration 5:{space 3}log pseudolikelihood = {res:-586.97792}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,615
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(15)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-586.97792{txt}{col 49}Pseudo R2{col 67}= {res}    0.4696

{txt}{ralign 81:(Std. Err. adjusted for {res:25} clusters in ccode)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}   prosanctions{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}loggedmigrants {c |}{col 17}{res}{space 2}-.2428046{col 29}{space 2} .1290413{col 40}{space 1}   -1.88{col 49}{space 3}0.060{col 57}{space 4} -.495721{col 70}{space 3} .0101117
{txt}{space 5}population {c |}{col 17}{res}{space 2} .3957735{col 29}{space 2} .2477749{col 40}{space 1}    1.60{col 49}{space 3}0.110{col 57}{space 4}-.0898564{col 70}{space 3} .8814035
{txt}{space 3}unemployment {c |}{col 17}{res}{space 2}-.1142117{col 29}{space 2} .0652266{col 40}{space 1}   -1.75{col 49}{space 3}0.080{col 57}{space 4}-.2420534{col 70}{space 3} .0136301
{txt}{space 6}gdpgrowth {c |}{col 17}{res}{space 2} .1265938{col 29}{space 2} .1386917{col 40}{space 1}    0.91{col 49}{space 3}0.361{col 57}{space 4} -.145237{col 70}{space 3} .3984247
{txt}{space 7}distance {c |}{col 17}{res}{space 2} .0005516{col 29}{space 2} .0003229{col 40}{space 1}    1.71{col 49}{space 3}0.088{col 57}{space 4}-.0000813{col 70}{space 3} .0011845
{txt}{space 12}col {c |}{col 17}{res}{space 2}  .912535{col 29}{space 2} .3603964{col 40}{space 1}    2.53{col 49}{space 3}0.011{col 57}{space 4} .2061709{col 70}{space 3} 1.618899
{txt}logged_refugees {c |}{col 17}{res}{space 2}-.0098513{col 29}{space 2} .1545155{col 40}{space 1}   -0.06{col 49}{space 3}0.949{col 57}{space 4}-.3126962{col 70}{space 3} .2929935
{txt}{space 6}libyabill {c |}{col 17}{res}{space 2} 3.778413{col 29}{space 2} .4017122{col 40}{space 1}    9.41{col 49}{space 3}0.000{col 57}{space 4} 2.991072{col 70}{space 3} 4.565755
{txt}{space 7}iranbill {c |}{col 17}{res}{space 2} 3.862235{col 29}{space 2} .4037159{col 40}{space 1}    9.57{col 49}{space 3}0.000{col 57}{space 4} 3.070967{col 70}{space 3} 4.653504
{txt}{space 15} {c |}
{space 10}ccode {c |}
{space 11}205  {c |}{col 17}{res}{space 2}  22.0642{col 29}{space 2} 14.62379{col 40}{space 1}    1.51{col 49}{space 3}0.131{col 57}{space 4}  -6.5979{col 70}{space 3} 50.72629
{txt}{space 11}210  {c |}{col 17}{res}{space 2} 19.51705{col 29}{space 2} 11.49655{col 40}{space 1}    1.70{col 49}{space 3}0.090{col 57}{space 4}-3.015774{col 70}{space 3} 42.04988
{txt}{space 11}211  {c |}{col 17}{res}{space 2} 19.65461{col 29}{space 2} 12.72269{col 40}{space 1}    1.54{col 49}{space 3}0.122{col 57}{space 4}-5.281409{col 70}{space 3} 44.59063
{txt}{space 11}212  {c |}{col 17}{res}{space 2} 23.04225{col 29}{space 2} 15.37912{col 40}{space 1}    1.50{col 49}{space 3}0.134{col 57}{space 4} -7.10027{col 70}{space 3} 53.18478
{txt}{space 11}220  {c |}{col 17}{res}{space 2}-1.272445{col 29}{space 2}  .503616{col 40}{space 1}   -2.53{col 49}{space 3}0.012{col 57}{space 4}-2.259514{col 70}{space 3}-.2853755
{txt}{space 11}230  {c |}{col 17}{res}{space 2} 7.605516{col 29}{space 2} 4.602886{col 40}{space 1}    1.65{col 49}{space 3}0.098{col 57}{space 4}-1.415976{col 70}{space 3} 16.62701
{txt}{space 11}235  {c |}{col 17}{res}{space 2}  20.7964{col 29}{space 2} 12.95597{col 40}{space 1}    1.61{col 49}{space 3}0.108{col 57}{space 4}-4.596831{col 70}{space 3} 46.18963
{txt}{space 11}255  {c |}{col 17}{res}{space 2}-6.728832{col 29}{space 2} 4.257185{col 40}{space 1}   -1.58{col 49}{space 3}0.114{col 57}{space 4}-15.07276{col 70}{space 3} 1.615097
{txt}{space 11}290  {c |}{col 17}{res}{space 2} 9.690234{col 29}{space 2} 6.587436{col 40}{space 1}    1.47{col 49}{space 3}0.141{col 57}{space 4}-3.220904{col 70}{space 3} 22.60137
{txt}{space 11}305  {c |}{col 17}{res}{space 2} 21.66025{col 29}{space 2} 13.68032{col 40}{space 1}    1.58{col 49}{space 3}0.113{col 57}{space 4}-5.152683{col 70}{space 3} 48.47319
{txt}{space 11}310  {c |}{col 17}{res}{space 2} 21.23207{col 29}{space 2} 13.65793{col 40}{space 1}    1.55{col 49}{space 3}0.120{col 57}{space 4}-5.536981{col 70}{space 3} 48.00113
{txt}{space 11}316  {c |}{col 17}{res}{space 2} 21.14694{col 29}{space 2} 13.08246{col 40}{space 1}    1.62{col 49}{space 3}0.106{col 57}{space 4}-4.494209{col 70}{space 3} 46.78808
{txt}{space 11}317  {c |}{col 17}{res}{space 2} 22.68579{col 29}{space 2} 14.75307{col 40}{space 1}    1.54{col 49}{space 3}0.124{col 57}{space 4}-6.229702{col 70}{space 3} 51.60129
{txt}{space 11}325  {c |}{col 17}{res}{space 2} 1.888076{col 29}{space 2} 1.173586{col 40}{space 1}    1.61{col 49}{space 3}0.108{col 57}{space 4}-.4121106{col 70}{space 3} 4.188264
{txt}{space 11}338  {c |}{col 17}{res}{space 2} 24.83863{col 29}{space 2} 15.96008{col 40}{space 1}    1.56{col 49}{space 3}0.120{col 57}{space 4}-6.442549{col 70}{space 3}  56.1198
{txt}{space 11}349  {c |}{col 17}{res}{space 2}        0{col 29}{txt}  (empty)
{space 11}350  {c |}{col 17}{res}{space 2} 22.47162{col 29}{space 2} 13.70985{col 40}{space 1}    1.64{col 49}{space 3}0.101{col 57}{space 4}-4.399194{col 70}{space 3} 49.34244
{txt}{space 11}352  {c |}{col 17}{res}{space 2} 26.46526{col 29}{space 2} 15.77679{col 40}{space 1}    1.68{col 49}{space 3}0.093{col 57}{space 4}-4.456673{col 70}{space 3}  57.3872
{txt}{space 11}355  {c |}{col 17}{res}{space 2} 22.58959{col 29}{space 2} 14.28049{col 40}{space 1}    1.58{col 49}{space 3}0.114{col 57}{space 4}-5.399655{col 70}{space 3} 50.57883
{txt}{space 11}360  {c |}{col 17}{res}{space 2}  17.6637{col 29}{space 2} 11.02042{col 40}{space 1}    1.60{col 49}{space 3}0.109{col 57}{space 4}-3.935939{col 70}{space 3} 39.26333
{txt}{space 11}367  {c |}{col 17}{res}{space 2} 23.17733{col 29}{space 2}  15.3091{col 40}{space 1}    1.51{col 49}{space 3}0.130{col 57}{space 4}-6.827949{col 70}{space 3}  53.1826
{txt}{space 11}368  {c |}{col 17}{res}{space 2} 22.55608{col 29}{space 2} 15.17602{col 40}{space 1}    1.49{col 49}{space 3}0.137{col 57}{space 4}-7.188369{col 70}{space 3} 52.30054
{txt}{space 11}375  {c |}{col 17}{res}{space 2} 23.60765{col 29}{space 2} 14.34377{col 40}{space 1}    1.65{col 49}{space 3}0.100{col 57}{space 4}-4.505628{col 70}{space 3} 51.72092
{txt}{space 11}380  {c |}{col 17}{res}{space 2} 20.96654{col 29}{space 2} 13.51941{col 40}{space 1}    1.55{col 49}{space 3}0.121{col 57}{space 4}-5.531015{col 70}{space 3} 47.46409
{txt}{space 11}390  {c |}{col 17}{res}{space 2} 22.80808{col 29}{space 2} 14.21728{col 40}{space 1}    1.60{col 49}{space 3}0.109{col 57}{space 4}-5.057274{col 70}{space 3} 50.67344
{txt}{space 15} {c |}
{space 10}party {c |}
{space 11}EPP  {c |}{col 17}{res}{space 2} 2.181544{col 29}{space 2}  .961906{col 40}{space 1}    2.27{col 49}{space 3}0.023{col 57}{space 4} .2962426{col 70}{space 3} 4.066845
{txt}{space 11}S&D  {c |}{col 17}{res}{space 2} 2.137934{col 29}{space 2} .9808704{col 40}{space 1}    2.18{col 49}{space 3}0.029{col 57}{space 4} .2154633{col 70}{space 3} 4.060405
{txt}{space 4}Greens/EFA  {c |}{col 17}{res}{space 2}-.9221133{col 29}{space 2}  .980303{col 40}{space 1}   -0.94{col 49}{space 3}0.347{col 57}{space 4}-2.843472{col 70}{space 3} .9992453
{txt}{space 5}ALDE/ADLE  {c |}{col 17}{res}{space 2} 2.223825{col 29}{space 2} 1.019668{col 40}{space 1}    2.18{col 49}{space 3}0.029{col 57}{space 4} .2253127{col 70}{space 3} 4.222337
{txt}{space 11}ECR  {c |}{col 17}{res}{space 2} 3.926459{col 29}{space 2} 1.013089{col 40}{space 1}    3.88{col 49}{space 3}0.000{col 57}{space 4} 1.940842{col 70}{space 3} 5.912076
{txt}{space 10}EFDD  {c |}{col 17}{res}{space 2}-.0119776{col 29}{space 2} .9208752{col 40}{space 1}   -0.01{col 49}{space 3}0.990{col 57}{space 4} -1.81686{col 70}{space 3} 1.792905
{txt}{space 7}GUE-NGL  {c |}{col 17}{res}{space 2} -1.29426{col 29}{space 2} 1.291221{col 40}{space 1}   -1.00{col 49}{space 3}0.316{col 57}{space 4}-3.825007{col 70}{space 3} 1.236487
{txt}{space 15} {c |}
{space 10}_cons {c |}{col 17}{res}{space 2}-27.80905{col 29}{space 2} 15.69464{col 40}{space 1}   -1.77{col 49}{space 3}0.076{col 57}{space 4}-58.56997{col 70}{space 3} 2.951875
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA13, title((A13))
{txt}
{com}. //Model A14
. logit prosanctions loggedmigrants population unemployment gdpgrowth distance col lnremit libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1087.5167}  
Iteration 1:{space 3}log pseudolikelihood = {res:-606.10467}  
Iteration 2:{space 3}log pseudolikelihood = {res:-599.25129}  
Iteration 3:{space 3}log pseudolikelihood = {res: -598.9177}  
Iteration 4:{space 3}log pseudolikelihood = {res:-598.91583}  
Iteration 5:{space 3}log pseudolikelihood = {res:-598.91583}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,576
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(15)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-598.91583{txt}{col 49}Pseudo R2{col 67}= {res}    0.4493

{txt}{ralign 80:(Std. Err. adjusted for {res:27} clusters in ccode)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}  prosanctions{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
loggedmigrants {c |}{col 16}{res}{space 2}-.3320088{col 28}{space 2} .1068593{col 39}{space 1}   -3.11{col 48}{space 3}0.002{col 56}{space 4}-.5414493{col 69}{space 3}-.1225684
{txt}{space 4}population {c |}{col 16}{res}{space 2} .7587729{col 28}{space 2} .3096765{col 39}{space 1}    2.45{col 48}{space 3}0.014{col 56}{space 4}  .151818{col 69}{space 3} 1.365728
{txt}{space 2}unemployment {c |}{col 16}{res}{space 2}-.1330392{col 28}{space 2} .0632876{col 39}{space 1}   -2.10{col 48}{space 3}0.036{col 56}{space 4}-.2570806{col 69}{space 3}-.0089978
{txt}{space 5}gdpgrowth {c |}{col 16}{res}{space 2} .0249187{col 28}{space 2} .1246798{col 39}{space 1}    0.20{col 48}{space 3}0.842{col 56}{space 4}-.2194492{col 69}{space 3} .2692866
{txt}{space 6}distance {c |}{col 16}{res}{space 2} .0007767{col 28}{space 2} .0003917{col 39}{space 1}    1.98{col 48}{space 3}0.047{col 56}{space 4} 8.93e-06{col 69}{space 3} .0015446
{txt}{space 11}col {c |}{col 16}{res}{space 2} 1.283182{col 28}{space 2} .4916813{col 39}{space 1}    2.61{col 48}{space 3}0.009{col 56}{space 4} .3195046{col 69}{space 3}  2.24686
{txt}{space 7}lnremit {c |}{col 16}{res}{space 2}-.2782657{col 28}{space 2} .1944346{col 39}{space 1}   -1.43{col 48}{space 3}0.152{col 56}{space 4}-.6593506{col 69}{space 3} .1028192
{txt}{space 5}libyabill {c |}{col 16}{res}{space 2} 3.220543{col 28}{space 2} .4969588{col 39}{space 1}    6.48{col 48}{space 3}0.000{col 56}{space 4} 2.246521{col 69}{space 3} 4.194564
{txt}{space 6}iranbill {c |}{col 16}{res}{space 2} 3.413714{col 28}{space 2} .3033983{col 39}{space 1}   11.25{col 48}{space 3}0.000{col 56}{space 4} 2.819064{col 69}{space 3} 4.008364
{txt}{space 14} {c |}
{space 9}ccode {c |}
{space 10}205  {c |}{col 16}{res}{space 2} 42.53642{col 28}{space 2} 17.74165{col 39}{space 1}    2.40{col 48}{space 3}0.017{col 56}{space 4} 7.763426{col 69}{space 3} 77.30942
{txt}{space 10}210  {c |}{col 16}{res}{space 2} 36.26603{col 28}{space 2} 14.35617{col 39}{space 1}    2.53{col 48}{space 3}0.012{col 56}{space 4} 8.128454{col 69}{space 3} 64.40361
{txt}{space 10}211  {c |}{col 16}{res}{space 2} 38.32238{col 28}{space 2} 15.82966{col 39}{space 1}    2.42{col 48}{space 3}0.015{col 56}{space 4} 7.296824{col 69}{space 3} 69.34794
{txt}{space 10}212  {c |}{col 16}{res}{space 2} 44.53281{col 28}{space 2} 18.95067{col 39}{space 1}    2.35{col 48}{space 3}0.019{col 56}{space 4} 7.390173{col 69}{space 3} 81.67545
{txt}{space 10}220  {c |}{col 16}{res}{space 2}-1.766212{col 28}{space 2} .6716008{col 39}{space 1}   -2.63{col 48}{space 3}0.009{col 56}{space 4}-3.082526{col 69}{space 3}-.4498989
{txt}{space 10}230  {c |}{col 16}{res}{space 2} 13.50171{col 28}{space 2} 5.536706{col 39}{space 1}    2.44{col 48}{space 3}0.015{col 56}{space 4}  2.64997{col 69}{space 3} 24.35346
{txt}{space 10}235  {c |}{col 16}{res}{space 2} 38.06608{col 28}{space 2} 15.66517{col 39}{space 1}    2.43{col 48}{space 3}0.015{col 56}{space 4} 7.362919{col 69}{space 3} 68.76924
{txt}{space 10}255  {c |}{col 16}{res}{space 2} -12.4375{col 28}{space 2} 5.148502{col 39}{space 1}   -2.42{col 48}{space 3}0.016{col 56}{space 4}-22.52838{col 69}{space 3} -2.34662
{txt}{space 10}290  {c |}{col 16}{res}{space 2} 18.61031{col 28}{space 2} 7.957629{col 39}{space 1}    2.34{col 48}{space 3}0.019{col 56}{space 4} 3.013645{col 69}{space 3} 34.20698
{txt}{space 10}305  {c |}{col 16}{res}{space 2} 41.54025{col 28}{space 2} 16.95619{col 39}{space 1}    2.45{col 48}{space 3}0.014{col 56}{space 4} 8.306728{col 69}{space 3} 74.77376
{txt}{space 10}310  {c |}{col 16}{res}{space 2} 40.60876{col 28}{space 2} 16.78208{col 39}{space 1}    2.42{col 48}{space 3}0.016{col 56}{space 4} 7.716495{col 69}{space 3} 73.50102
{txt}{space 10}316  {c |}{col 16}{res}{space 2} 39.45109{col 28}{space 2} 16.11516{col 39}{space 1}    2.45{col 48}{space 3}0.014{col 56}{space 4} 7.865966{col 69}{space 3} 71.03622
{txt}{space 10}317  {c |}{col 16}{res}{space 2} 43.22174{col 28}{space 2} 18.15143{col 39}{space 1}    2.38{col 48}{space 3}0.017{col 56}{space 4} 7.645592{col 69}{space 3} 78.79789
{txt}{space 10}325  {c |}{col 16}{res}{space 2} 3.346263{col 28}{space 2}  1.39639{col 39}{space 1}    2.40{col 48}{space 3}0.017{col 56}{space 4} .6093883{col 69}{space 3} 6.083137
{txt}{space 10}338  {c |}{col 16}{res}{space 2} 45.47039{col 28}{space 2} 19.26503{col 39}{space 1}    2.36{col 48}{space 3}0.018{col 56}{space 4} 7.711638{col 69}{space 3} 83.22915
{txt}{space 10}349  {c |}{col 16}{res}{space 2} 44.49724{col 28}{space 2} 18.47128{col 39}{space 1}    2.41{col 48}{space 3}0.016{col 56}{space 4} 8.294198{col 69}{space 3} 80.70029
{txt}{space 10}350  {c |}{col 16}{res}{space 2} 41.44995{col 28}{space 2} 16.89457{col 39}{space 1}    2.45{col 48}{space 3}0.014{col 56}{space 4} 8.337196{col 69}{space 3}  74.5627
{txt}{space 10}352  {c |}{col 16}{res}{space 2} 49.13002{col 28}{space 2} 19.69551{col 39}{space 1}    2.49{col 48}{space 3}0.013{col 56}{space 4} 10.52753{col 69}{space 3} 87.73252
{txt}{space 10}355  {c |}{col 16}{res}{space 2} 41.77898{col 28}{space 2} 17.48472{col 39}{space 1}    2.39{col 48}{space 3}0.017{col 56}{space 4} 7.509548{col 69}{space 3}  76.0484
{txt}{space 10}360  {c |}{col 16}{res}{space 2} 33.38125{col 28}{space 2} 13.58628{col 39}{space 1}    2.46{col 48}{space 3}0.014{col 56}{space 4} 6.752626{col 69}{space 3} 60.00987
{txt}{space 10}366  {c |}{col 16}{res}{space 2} 43.84623{col 28}{space 2} 18.56533{col 39}{space 1}    2.36{col 48}{space 3}0.018{col 56}{space 4} 7.458846{col 69}{space 3} 80.23361
{txt}{space 10}367  {c |}{col 16}{res}{space 2} 45.50389{col 28}{space 2} 18.77612{col 39}{space 1}    2.42{col 48}{space 3}0.015{col 56}{space 4} 8.703376{col 69}{space 3} 82.30441
{txt}{space 10}368  {c |}{col 16}{res}{space 2} 43.79849{col 28}{space 2} 18.60876{col 39}{space 1}    2.35{col 48}{space 3}0.019{col 56}{space 4} 7.325997{col 69}{space 3} 80.27098
{txt}{space 10}375  {c |}{col 16}{res}{space 2} 43.97356{col 28}{space 2} 17.75646{col 39}{space 1}    2.48{col 48}{space 3}0.013{col 56}{space 4}  9.17154{col 69}{space 3} 78.77559
{txt}{space 10}380  {c |}{col 16}{res}{space 2} 41.01965{col 28}{space 2} 16.94144{col 39}{space 1}    2.42{col 48}{space 3}0.015{col 56}{space 4} 7.815045{col 69}{space 3} 74.22426
{txt}{space 10}390  {c |}{col 16}{res}{space 2} 43.50792{col 28}{space 2} 17.75945{col 39}{space 1}    2.45{col 48}{space 3}0.014{col 56}{space 4} 8.700035{col 69}{space 3}  78.3158
{txt}{space 14} {c |}
{space 9}party {c |}
{space 10}EPP  {c |}{col 16}{res}{space 2} 1.863859{col 28}{space 2} 1.023988{col 39}{space 1}    1.82{col 48}{space 3}0.069{col 56}{space 4}  -.14312{col 69}{space 3} 3.870838
{txt}{space 10}S&D  {c |}{col 16}{res}{space 2} 1.898342{col 28}{space 2} 1.037115{col 39}{space 1}    1.83{col 48}{space 3}0.067{col 56}{space 4}-.1343654{col 69}{space 3}  3.93105
{txt}{space 3}Greens/EFA  {c |}{col 16}{res}{space 2}-.9422344{col 28}{space 2} .9887585{col 39}{space 1}   -0.95{col 48}{space 3}0.341{col 56}{space 4}-2.880166{col 69}{space 3} .9956968
{txt}{space 4}ALDE/ADLE  {c |}{col 16}{res}{space 2} 2.029044{col 28}{space 2} 1.049154{col 39}{space 1}    1.93{col 48}{space 3}0.053{col 56}{space 4}-.0272608{col 69}{space 3} 4.085348
{txt}{space 10}ECR  {c |}{col 16}{res}{space 2} 3.726655{col 28}{space 2} 1.027119{col 39}{space 1}    3.63{col 48}{space 3}0.000{col 56}{space 4}  1.71354{col 69}{space 3} 5.739771
{txt}{space 9}EFDD  {c |}{col 16}{res}{space 2}-.2524234{col 28}{space 2} .9640174{col 39}{space 1}   -0.26{col 48}{space 3}0.793{col 56}{space 4}-2.141863{col 69}{space 3} 1.637016
{txt}{space 6}GUE-NGL  {c |}{col 16}{res}{space 2}-.9296742{col 28}{space 2} 1.302255{col 39}{space 1}   -0.71{col 48}{space 3}0.475{col 56}{space 4}-3.482048{col 69}{space 3} 1.622699
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2}-49.39447{col 28}{space 2} 19.07603{col 39}{space 1}   -2.59{col 48}{space 3}0.010{col 56}{space 4}-86.78279{col 69}{space 3}-12.00614
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA14, title((A14))
{txt}
{com}. 
. estout MODELA7 MODELA8 MODELA9 MODELA10 MODELA11 MODELA12 MODELA13 MODELA14, cells(b(star fmt(3)) se(par fmt(3))) starlevels($^{c -(}+{c )-}$ 0.10 $^{c -(}*{c )-}$ 0.05 $^{c -(}**{c )-}$ 0.01 $^{c -(}***{c )-}$ 0.001) stats(N N_g r2, fmt(0 0 3) label(Observations Countries R$^2$)) label,  using "modelsa7_a14.tex", replace style(tex)
{res}{txt}(note: file modelsa7_a14.tex not found)
(output written to {browse  `"modelsa7_a14.tex"'})

{com}. 
. **TABLE A4
. //Model A15
. logit prosanctions migrantshare, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1171.9868}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1171.9868}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      0.33
{txt}{col 49}Prob > chi2{col 67}= {res}    0.5642
{txt}Log pseudolikelihood = {res}-1171.9868{txt}{col 49}Pseudo R2{col 67}= {res}    0.0002

{txt}{ralign 78:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}prosanctions{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
migrantshare {c |}{col 14}{res}{space 2} -.443988{col 26}{space 2} .7700219{col 37}{space 1}   -0.58{col 46}{space 3}0.564{col 54}{space 4}-1.953203{col 67}{space 3} 1.065227
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2841064{col 26}{space 2}  .107008{col 37}{space 1}    2.66{col 46}{space 3}0.008{col 54}{space 4} .0743746{col 67}{space 3} .4938382
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA15, title((A15))
{txt}
{com}. //Model A16
. logit prosanctions migrantshare libyabill iranbill, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-829.44244}  
Iteration 2:{space 3}log pseudolikelihood = {res:-828.45946}  
Iteration 3:{space 3}log pseudolikelihood = {res: -828.4572}  
Iteration 4:{space 3}log pseudolikelihood = {res: -828.4572}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}Wald chi2({res}3{txt}){col 67}= {res}    161.17
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res} -828.4572{txt}{col 49}Pseudo R2{col 67}= {res}    0.2933

{txt}{ralign 78:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}prosanctions{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
migrantshare {c |}{col 14}{res}{space 2}-2.368881{col 26}{space 2} .8043202{col 37}{space 1}   -2.95{col 46}{space 3}0.003{col 54}{space 4} -3.94532{col 67}{space 3}-.7924423
{txt}{space 3}libyabill {c |}{col 14}{res}{space 2} 3.012465{col 26}{space 2} .2702762{col 37}{space 1}   11.15{col 46}{space 3}0.000{col 54}{space 4} 2.482733{col 67}{space 3} 3.542196
{txt}{space 4}iranbill {c |}{col 14}{res}{space 2} 3.323728{col 26}{space 2} .3549625{col 37}{space 1}    9.36{col 46}{space 3}0.000{col 54}{space 4} 2.628014{col 67}{space 3} 4.019441
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.795519{col 26}{space 2} .1628911{col 37}{space 1}  -11.02{col 46}{space 3}0.000{col 54}{space 4} -2.11478{col 67}{space 3}-1.476259
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA16, title((A16))
{txt}
{com}. //Model A17
. logit prosanctions migrantshare libyabill iranbill i.ccode, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-792.10724}  
Iteration 2:{space 3}log pseudolikelihood = {res:-787.79801}  
Iteration 3:{space 3}log pseudolikelihood = {res:-787.77677}  
Iteration 4:{space 3}log pseudolikelihood = {res:-787.77676}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(3)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-787.77676{txt}{col 49}Pseudo R2{col 67}= {res}    0.3280

{txt}{ralign 78:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}prosanctions{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
migrantshare {c |}{col 14}{res}{space 2}-3.736702{col 26}{space 2} 1.567036{col 37}{space 1}   -2.38{col 46}{space 3}0.017{col 54}{space 4}-6.808036{col 67}{space 3}-.6653684
{txt}{space 3}libyabill {c |}{col 14}{res}{space 2} 3.134628{col 26}{space 2} .2865661{col 37}{space 1}   10.94{col 46}{space 3}0.000{col 54}{space 4} 2.572969{col 67}{space 3} 3.696288
{txt}{space 4}iranbill {c |}{col 14}{res}{space 2} 3.498713{col 26}{space 2} .3763466{col 37}{space 1}    9.30{col 46}{space 3}0.000{col 54}{space 4} 2.761087{col 67}{space 3} 4.236339
{txt}{space 12} {c |}
{space 7}ccode {c |}
{space 8}205  {c |}{col 14}{res}{space 2}-1.544746{col 26}{space 2} .0956622{col 37}{space 1}  -16.15{col 46}{space 3}0.000{col 54}{space 4}-1.732241{col 67}{space 3}-1.357252
{txt}{space 8}210  {c |}{col 14}{res}{space 2}-.0040372{col 26}{space 2} .0423125{col 37}{space 1}   -0.10{col 46}{space 3}0.924{col 54}{space 4}-.0869682{col 67}{space 3} .0788938
{txt}{space 8}211  {c |}{col 14}{res}{space 2}-.6842691{col 26}{space 2}  .080546{col 37}{space 1}   -8.50{col 46}{space 3}0.000{col 54}{space 4}-.8421364{col 67}{space 3}-.5264017
{txt}{space 8}212  {c |}{col 14}{res}{space 2}-.4978118{col 26}{space 2} .0640959{col 37}{space 1}   -7.77{col 46}{space 3}0.000{col 54}{space 4}-.6234374{col 67}{space 3}-.3721862
{txt}{space 8}220  {c |}{col 14}{res}{space 2}-1.535812{col 26}{space 2} .1021526{col 37}{space 1}  -15.03{col 46}{space 3}0.000{col 54}{space 4}-1.736027{col 67}{space 3}-1.335596
{txt}{space 8}230  {c |}{col 14}{res}{space 2}-.8158177{col 26}{space 2} .0888086{col 37}{space 1}   -9.19{col 46}{space 3}0.000{col 54}{space 4}-.9898793{col 67}{space 3}-.6417561
{txt}{space 8}235  {c |}{col 14}{res}{space 2}-.2472525{col 26}{space 2} .0777609{col 37}{space 1}   -3.18{col 46}{space 3}0.001{col 54}{space 4}-.3996611{col 67}{space 3}-.0948438
{txt}{space 8}255  {c |}{col 14}{res}{space 2}-.5911865{col 26}{space 2} .0122216{col 37}{space 1}  -48.37{col 46}{space 3}0.000{col 54}{space 4}-.6151405{col 67}{space 3}-.5672326
{txt}{space 8}290  {c |}{col 14}{res}{space 2}-.0382089{col 26}{space 2} .0778393{col 37}{space 1}   -0.49{col 46}{space 3}0.624{col 54}{space 4}-.1907711{col 67}{space 3} .1143533
{txt}{space 8}305  {c |}{col 14}{res}{space 2}-.8206704{col 26}{space 2} .0297384{col 37}{space 1}  -27.60{col 46}{space 3}0.000{col 54}{space 4}-.8789566{col 67}{space 3}-.7623842
{txt}{space 8}310  {c |}{col 14}{res}{space 2} -.545553{col 26}{space 2} .0670155{col 37}{space 1}   -8.14{col 46}{space 3}0.000{col 54}{space 4} -.676901{col 67}{space 3}-.4142049
{txt}{space 8}316  {c |}{col 14}{res}{space 2}  .021556{col 26}{space 2} .0788353{col 37}{space 1}    0.27{col 46}{space 3}0.785{col 54}{space 4}-.1329583{col 67}{space 3} .1760703
{txt}{space 8}317  {c |}{col 14}{res}{space 2}-.2445341{col 26}{space 2} .0762412{col 37}{space 1}   -3.21{col 46}{space 3}0.001{col 54}{space 4}-.3939641{col 67}{space 3}-.0951041
{txt}{space 8}325  {c |}{col 14}{res}{space 2}  -.90779{col 26}{space 2} .0427154{col 37}{space 1}  -21.25{col 46}{space 3}0.000{col 54}{space 4}-.9915107{col 67}{space 3}-.8240692
{txt}{space 8}338  {c |}{col 14}{res}{space 2} .5191314{col 26}{space 2}  .170442{col 37}{space 1}    3.05{col 46}{space 3}0.002{col 54}{space 4} .1850713{col 67}{space 3} .8531916
{txt}{space 8}344  {c |}{col 14}{res}{space 2} .8962499{col 26}{space 2} .1439776{col 37}{space 1}    6.22{col 46}{space 3}0.000{col 54}{space 4}  .614059{col 67}{space 3} 1.178441
{txt}{space 8}349  {c |}{col 14}{res}{space 2}-.2406057{col 26}{space 2} .0776801{col 37}{space 1}   -3.10{col 46}{space 3}0.002{col 54}{space 4}-.3928559{col 67}{space 3}-.0883555
{txt}{space 8}350  {c |}{col 14}{res}{space 2}-2.165492{col 26}{space 2} .0990979{col 37}{space 1}  -21.85{col 46}{space 3}0.000{col 54}{space 4} -2.35972{col 67}{space 3}-1.971263
{txt}{space 8}352  {c |}{col 14}{res}{space 2}  -.66798{col 26}{space 2} .0492153{col 37}{space 1}  -13.57{col 46}{space 3}0.000{col 54}{space 4}-.7644402{col 67}{space 3}-.5715199
{txt}{space 8}355  {c |}{col 14}{res}{space 2}-.2966039{col 26}{space 2} .0683517{col 37}{space 1}   -4.34{col 46}{space 3}0.000{col 54}{space 4}-.4305709{col 67}{space 3} -.162637
{txt}{space 8}360  {c |}{col 14}{res}{space 2}  .094716{col 26}{space 2} .0752211{col 37}{space 1}    1.26{col 46}{space 3}0.208{col 54}{space 4}-.0527146{col 67}{space 3} .2421466
{txt}{space 8}366  {c |}{col 14}{res}{space 2}-.5124363{col 26}{space 2} .0808865{col 37}{space 1}   -6.34{col 46}{space 3}0.000{col 54}{space 4} -.670971{col 67}{space 3}-.3539016
{txt}{space 8}367  {c |}{col 14}{res}{space 2}-.6127205{col 26}{space 2} .1070259{col 37}{space 1}   -5.72{col 46}{space 3}0.000{col 54}{space 4}-.8224875{col 67}{space 3}-.4029536
{txt}{space 8}368  {c |}{col 14}{res}{space 2}-.2298051{col 26}{space 2} .0786698{col 37}{space 1}   -2.92{col 46}{space 3}0.003{col 54}{space 4}-.3839952{col 67}{space 3}-.0756151
{txt}{space 8}375  {c |}{col 14}{res}{space 2} .3727819{col 26}{space 2} .1036966{col 37}{space 1}    3.59{col 46}{space 3}0.000{col 54}{space 4} .1695404{col 67}{space 3} .5760235
{txt}{space 8}380  {c |}{col 14}{res}{space 2} .2436645{col 26}{space 2} .6084909{col 37}{space 1}    0.40{col 46}{space 3}0.689{col 54}{space 4}-.9489558{col 67}{space 3} 1.436285
{txt}{space 8}390  {c |}{col 14}{res}{space 2}-.2566525{col 26}{space 2} .0832762{col 37}{space 1}   -3.08{col 46}{space 3}0.002{col 54}{space 4}-.4198709{col 67}{space 3}-.0934341
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-1.279624{col 26}{space 2} .2006811{col 37}{space 1}   -6.38{col 46}{space 3}0.000{col 54}{space 4}-1.672952{col 67}{space 3}-.8862963
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA17, title((A17))
{txt}
{com}. //Model A18
. logit prosanctions migrantshare libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-638.63625}  
Iteration 2:{space 3}log pseudolikelihood = {res:-630.59197}  
Iteration 3:{space 3}log pseudolikelihood = {res:-630.20438}  
Iteration 4:{space 3}log pseudolikelihood = {res:-630.19992}  
Iteration 5:{space 3}log pseudolikelihood = {res:-630.19992}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(9)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-630.19992{txt}{col 49}Pseudo R2{col 67}= {res}    0.4624

{txt}{ralign 78:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}prosanctions{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
migrantshare {c |}{col 14}{res}{space 2}-4.233529{col 26}{space 2} .9571011{col 37}{space 1}   -4.42{col 46}{space 3}0.000{col 54}{space 4}-6.109413{col 67}{space 3}-2.357646
{txt}{space 3}libyabill {c |}{col 14}{res}{space 2} 3.820211{col 26}{space 2}   .32038{col 37}{space 1}   11.92{col 46}{space 3}0.000{col 54}{space 4} 3.192278{col 67}{space 3} 4.448144
{txt}{space 4}iranbill {c |}{col 14}{res}{space 2}  4.09232{col 26}{space 2} .3895374{col 37}{space 1}   10.51{col 46}{space 3}0.000{col 54}{space 4}  3.32884{col 67}{space 3} 4.855799
{txt}{space 12} {c |}
{space 7}ccode {c |}
{space 8}205  {c |}{col 14}{res}{space 2}-1.006707{col 26}{space 2}  .096842{col 37}{space 1}  -10.40{col 46}{space 3}0.000{col 54}{space 4}-1.196514{col 67}{space 3}-.8169002
{txt}{space 8}210  {c |}{col 14}{res}{space 2} 1.021105{col 26}{space 2} .1740088{col 37}{space 1}    5.87{col 46}{space 3}0.000{col 54}{space 4} .6800536{col 67}{space 3} 1.362156
{txt}{space 8}211  {c |}{col 14}{res}{space 2}-.5349366{col 26}{space 2} .0821112{col 37}{space 1}   -6.51{col 46}{space 3}0.000{col 54}{space 4}-.6958716{col 67}{space 3}-.3740016
{txt}{space 8}212  {c |}{col 14}{res}{space 2}-.1336883{col 26}{space 2} .0733985{col 37}{space 1}   -1.82{col 46}{space 3}0.069{col 54}{space 4}-.2775467{col 67}{space 3}   .01017
{txt}{space 8}220  {c |}{col 14}{res}{space 2}-.8772038{col 26}{space 2} .1966984{col 37}{space 1}   -4.46{col 46}{space 3}0.000{col 54}{space 4}-1.262726{col 67}{space 3}-.4916821
{txt}{space 8}230  {c |}{col 14}{res}{space 2}-.4766628{col 26}{space 2} .1234555{col 37}{space 1}   -3.86{col 46}{space 3}0.000{col 54}{space 4}-.7186312{col 67}{space 3}-.2346944
{txt}{space 8}235  {c |}{col 14}{res}{space 2} .1623777{col 26}{space 2}  .148001{col 37}{space 1}    1.10{col 46}{space 3}0.273{col 54}{space 4} -.127699{col 67}{space 3} .4524543
{txt}{space 8}255  {c |}{col 14}{res}{space 2}-.0241914{col 26}{space 2} .1003641{col 37}{space 1}   -0.24{col 46}{space 3}0.810{col 54}{space 4}-.2209013{col 67}{space 3} .1725186
{txt}{space 8}290  {c |}{col 14}{res}{space 2}-.3212857{col 26}{space 2} .1342974{col 37}{space 1}   -2.39{col 46}{space 3}0.017{col 54}{space 4}-.5845038{col 67}{space 3}-.0580676
{txt}{space 8}305  {c |}{col 14}{res}{space 2}-.1196498{col 26}{space 2} .2542463{col 37}{space 1}   -0.47{col 46}{space 3}0.638{col 54}{space 4}-.6179633{col 67}{space 3} .3786638
{txt}{space 8}310  {c |}{col 14}{res}{space 2}-.3822543{col 26}{space 2} .1358885{col 37}{space 1}   -2.81{col 46}{space 3}0.005{col 54}{space 4} -.648591{col 67}{space 3}-.1159176
{txt}{space 8}316  {c |}{col 14}{res}{space 2} .2375833{col 26}{space 2}  .165686{col 37}{space 1}    1.43{col 46}{space 3}0.152{col 54}{space 4}-.0871553{col 67}{space 3} .5623219
{txt}{space 8}317  {c |}{col 14}{res}{space 2}-.4973473{col 26}{space 2} .1113639{col 37}{space 1}   -4.47{col 46}{space 3}0.000{col 54}{space 4}-.7156165{col 67}{space 3}-.2790782
{txt}{space 8}325  {c |}{col 14}{res}{space 2} -.654906{col 26}{space 2} .1496283{col 37}{space 1}   -4.38{col 46}{space 3}0.000{col 54}{space 4}-.9481721{col 67}{space 3}  -.36164
{txt}{space 8}338  {c |}{col 14}{res}{space 2} .3340268{col 26}{space 2}  .131385{col 37}{space 1}    2.54{col 46}{space 3}0.011{col 54}{space 4} .0765168{col 67}{space 3} .5915367
{txt}{space 8}344  {c |}{col 14}{res}{space 2} .8226737{col 26}{space 2} .1473195{col 37}{space 1}    5.58{col 46}{space 3}0.000{col 54}{space 4} .5339328{col 67}{space 3} 1.111415
{txt}{space 8}349  {c |}{col 14}{res}{space 2}-.3204934{col 26}{space 2} .0839786{col 37}{space 1}   -3.82{col 46}{space 3}0.000{col 54}{space 4}-.4850883{col 67}{space 3}-.1558985
{txt}{space 8}350  {c |}{col 14}{res}{space 2}-1.324878{col 26}{space 2} .1350135{col 37}{space 1}   -9.81{col 46}{space 3}0.000{col 54}{space 4}-1.589499{col 67}{space 3}-1.060256
{txt}{space 8}352  {c |}{col 14}{res}{space 2} .2643244{col 26}{space 2} .2658672{col 37}{space 1}    0.99{col 46}{space 3}0.320{col 54}{space 4}-.2567656{col 67}{space 3} .7854145
{txt}{space 8}355  {c |}{col 14}{res}{space 2}-.5779783{col 26}{space 2} .0962604{col 37}{space 1}   -6.00{col 46}{space 3}0.000{col 54}{space 4}-.7666453{col 67}{space 3}-.3893113
{txt}{space 8}360  {c |}{col 14}{res}{space 2}-.0855434{col 26}{space 2} .0674206{col 37}{space 1}   -1.27{col 46}{space 3}0.205{col 54}{space 4}-.2176852{col 67}{space 3} .0465985
{txt}{space 8}366  {c |}{col 14}{res}{space 2}-.5076114{col 26}{space 2} .1056393{col 37}{space 1}   -4.81{col 46}{space 3}0.000{col 54}{space 4}-.7146607{col 67}{space 3}-.3005621
{txt}{space 8}367  {c |}{col 14}{res}{space 2}-.1662305{col 26}{space 2} .1222971{col 37}{space 1}   -1.36{col 46}{space 3}0.174{col 54}{space 4}-.4059285{col 67}{space 3} .0734675
{txt}{space 8}368  {c |}{col 14}{res}{space 2}-.2219702{col 26}{space 2} .1507872{col 37}{space 1}   -1.47{col 46}{space 3}0.141{col 54}{space 4}-.5175077{col 67}{space 3} .0735673
{txt}{space 8}375  {c |}{col 14}{res}{space 2}  .892785{col 26}{space 2} .1238389{col 37}{space 1}    7.21{col 46}{space 3}0.000{col 54}{space 4} .6500652{col 67}{space 3} 1.135505
{txt}{space 8}380  {c |}{col 14}{res}{space 2} .8022501{col 26}{space 2} .3290228{col 37}{space 1}    2.44{col 46}{space 3}0.015{col 54}{space 4} .1573772{col 67}{space 3} 1.447123
{txt}{space 8}390  {c |}{col 14}{res}{space 2} .1825887{col 26}{space 2} .1089659{col 37}{space 1}    1.68{col 46}{space 3}0.094{col 54}{space 4}-.0309805{col 67}{space 3} .3961579
{txt}{space 12} {c |}
{space 7}party {c |}
{space 8}EPP  {c |}{col 14}{res}{space 2} 2.250474{col 26}{space 2} .9608392{col 37}{space 1}    2.34{col 46}{space 3}0.019{col 54}{space 4} .3672634{col 67}{space 3} 4.133684
{txt}{space 8}S&D  {c |}{col 14}{res}{space 2} 2.220338{col 26}{space 2} .9781987{col 37}{space 1}    2.27{col 46}{space 3}0.023{col 54}{space 4} .3031034{col 67}{space 3} 4.137572
{txt}{space 1}Greens/EFA  {c |}{col 14}{res}{space 2}-.8088489{col 26}{space 2} .9658064{col 37}{space 1}   -0.84{col 46}{space 3}0.402{col 54}{space 4}-2.701795{col 67}{space 3} 1.084097
{txt}{space 2}ALDE/ADLE  {c |}{col 14}{res}{space 2} 2.377371{col 26}{space 2}  1.00637{col 37}{space 1}    2.36{col 46}{space 3}0.018{col 54}{space 4}  .404922{col 67}{space 3} 4.349819
{txt}{space 8}ECR  {c |}{col 14}{res}{space 2} 4.117501{col 26}{space 2} 1.017686{col 37}{space 1}    4.05{col 46}{space 3}0.000{col 54}{space 4} 2.122873{col 67}{space 3} 6.112129
{txt}{space 7}EFDD  {c |}{col 14}{res}{space 2} .2014028{col 26}{space 2} .9555298{col 37}{space 1}    0.21{col 46}{space 3}0.833{col 54}{space 4}-1.671401{col 67}{space 3} 2.074207
{txt}{space 4}GUE-NGL  {c |}{col 14}{res}{space 2}-.9465976{col 26}{space 2} 1.237779{col 37}{space 1}   -0.76{col 46}{space 3}0.444{col 54}{space 4}  -3.3726{col 67}{space 3} 1.479405
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-3.616918{col 26}{space 2} .9637976{col 37}{space 1}   -3.75{col 46}{space 3}0.000{col 54}{space 4}-5.505927{col 67}{space 3}-1.727909
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA18, title((A18))
{txt}
{com}. //Model A19
. logit prosanctions migrantshare population unemployment gdpgrowth distance col libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-634.64366}  
Iteration 2:{space 3}log pseudolikelihood = {res:-626.19348}  
Iteration 3:{space 3}log pseudolikelihood = {res:-625.77991}  
Iteration 4:{space 3}log pseudolikelihood = {res: -625.7749}  
Iteration 5:{space 3}log pseudolikelihood = {res: -625.7749}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(14)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res} -625.7749{txt}{col 49}Pseudo R2{col 67}= {res}    0.4662

{txt}{ralign 78:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}prosanctions{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
migrantshare {c |}{col 14}{res}{space 2}-5.023559{col 26}{space 2} 1.505006{col 37}{space 1}   -3.34{col 46}{space 3}0.001{col 54}{space 4}-7.973317{col 67}{space 3}-2.073802
{txt}{space 2}population {c |}{col 14}{res}{space 2} .0256494{col 26}{space 2} .1631912{col 37}{space 1}    0.16{col 46}{space 3}0.875{col 54}{space 4}-.2941995{col 67}{space 3} .3454983
{txt}unemployment {c |}{col 14}{res}{space 2}-.0974384{col 26}{space 2} .0621422{col 37}{space 1}   -1.57{col 46}{space 3}0.117{col 54}{space 4}-.2192348{col 67}{space 3}  .024358
{txt}{space 3}gdpgrowth {c |}{col 14}{res}{space 2} .1436104{col 26}{space 2} .1085305{col 37}{space 1}    1.32{col 46}{space 3}0.186{col 54}{space 4}-.0691055{col 67}{space 3} .3563262
{txt}{space 4}distance {c |}{col 14}{res}{space 2}-.0000642{col 26}{space 2}   .00023{col 37}{space 1}   -0.28{col 46}{space 3}0.780{col 54}{space 4}-.0005151{col 67}{space 3} .0003866
{txt}{space 9}col {c |}{col 14}{res}{space 2} .5667068{col 26}{space 2} .2041282{col 37}{space 1}    2.78{col 46}{space 3}0.005{col 54}{space 4} .1666229{col 67}{space 3} .9667907
{txt}{space 3}libyabill {c |}{col 14}{res}{space 2} 3.866864{col 26}{space 2} .3908737{col 37}{space 1}    9.89{col 46}{space 3}0.000{col 54}{space 4} 3.100766{col 67}{space 3} 4.632962
{txt}{space 4}iranbill {c |}{col 14}{res}{space 2} 4.631664{col 26}{space 2} .5128205{col 37}{space 1}    9.03{col 46}{space 3}0.000{col 54}{space 4} 3.626555{col 67}{space 3} 5.636774
{txt}{space 12} {c |}
{space 7}ccode {c |}
{space 8}205  {c |}{col 14}{res}{space 2} .8375226{col 26}{space 2} 9.822363{col 37}{space 1}    0.09{col 46}{space 3}0.932{col 54}{space 4}-18.41395{col 67}{space 3}   20.089
{txt}{space 8}210  {c |}{col 14}{res}{space 2}   2.3568{col 26}{space 2} 7.607885{col 37}{space 1}    0.31{col 46}{space 3}0.757{col 54}{space 4}-12.55438{col 67}{space 3} 17.26798
{txt}{space 8}211  {c |}{col 14}{res}{space 2} 1.008702{col 26}{space 2} 8.561047{col 37}{space 1}    0.12{col 46}{space 3}0.906{col 54}{space 4}-15.77064{col 67}{space 3} 17.78805
{txt}{space 8}212  {c |}{col 14}{res}{space 2} .9081814{col 26}{space 2} 10.37835{col 37}{space 1}    0.09{col 46}{space 3}0.930{col 54}{space 4}-19.43301{col 67}{space 3} 21.24937
{txt}{space 8}220  {c |}{col 14}{res}{space 2}-.6423222{col 26}{space 2} .4218665{col 37}{space 1}   -1.52{col 46}{space 3}0.128{col 54}{space 4}-1.469165{col 67}{space 3} .1845208
{txt}{space 8}230  {c |}{col 14}{res}{space 2} 1.811717{col 26}{space 2} 3.318116{col 37}{space 1}    0.55{col 46}{space 3}0.585{col 54}{space 4}-4.691672{col 67}{space 3} 8.315106
{txt}{space 8}235  {c |}{col 14}{res}{space 2} 2.392015{col 26}{space 2} 8.754313{col 37}{space 1}    0.27{col 46}{space 3}0.785{col 54}{space 4}-14.76612{col 67}{space 3} 19.55015
{txt}{space 8}255  {c |}{col 14}{res}{space 2}-.6056441{col 26}{space 2} 2.915445{col 37}{space 1}   -0.21{col 46}{space 3}0.835{col 54}{space 4}-6.319812{col 67}{space 3} 5.108524
{txt}{space 8}290  {c |}{col 14}{res}{space 2} .4012189{col 26}{space 2} 4.321385{col 37}{space 1}    0.09{col 46}{space 3}0.926{col 54}{space 4}-8.068539{col 67}{space 3} 8.870977
{txt}{space 8}305  {c |}{col 14}{res}{space 2} 1.237371{col 26}{space 2} 8.981747{col 37}{space 1}    0.14{col 46}{space 3}0.890{col 54}{space 4}-16.36653{col 67}{space 3} 18.84127
{txt}{space 8}310  {c |}{col 14}{res}{space 2} 1.115713{col 26}{space 2} 8.912449{col 37}{space 1}    0.13{col 46}{space 3}0.900{col 54}{space 4}-16.35237{col 67}{space 3} 18.58379
{txt}{space 8}316  {c |}{col 14}{res}{space 2} 1.607721{col 26}{space 2} 8.521189{col 37}{space 1}    0.19{col 46}{space 3}0.850{col 54}{space 4} -15.0935{col 67}{space 3} 18.30895
{txt}{space 8}317  {c |}{col 14}{res}{space 2} 1.420291{col 26}{space 2} 9.810151{col 37}{space 1}    0.14{col 46}{space 3}0.885{col 54}{space 4}-17.80725{col 67}{space 3} 20.64783
{txt}{space 8}325  {c |}{col 14}{res}{space 2}-.1422431{col 26}{space 2} .7671075{col 37}{space 1}   -0.19{col 46}{space 3}0.853{col 54}{space 4}-1.645746{col 67}{space 3}  1.36126
{txt}{space 8}338  {c |}{col 14}{res}{space 2} 1.732143{col 26}{space 2} 10.47591{col 37}{space 1}    0.17{col 46}{space 3}0.869{col 54}{space 4}-18.80027{col 67}{space 3} 22.26456
{txt}{space 8}344  {c |}{col 14}{res}{space 2} 3.608985{col 26}{space 2} 9.927353{col 37}{space 1}    0.36{col 46}{space 3}0.716{col 54}{space 4}-15.84827{col 67}{space 3} 23.06624
{txt}{space 8}349  {c |}{col 14}{res}{space 2} 1.465624{col 26}{space 2} 10.05895{col 37}{space 1}    0.15{col 46}{space 3}0.884{col 54}{space 4}-18.24955{col 67}{space 3}  21.1808
{txt}{space 8}350  {c |}{col 14}{res}{space 2} 2.082938{col 26}{space 2} 8.978115{col 37}{space 1}    0.23{col 46}{space 3}0.817{col 54}{space 4}-15.51384{col 67}{space 3} 19.67972
{txt}{space 8}352  {c |}{col 14}{res}{space 2} 2.999773{col 26}{space 2} 10.20401{col 37}{space 1}    0.29{col 46}{space 3}0.769{col 54}{space 4}-16.99972{col 67}{space 3} 22.99927
{txt}{space 8}355  {c |}{col 14}{res}{space 2} 1.316311{col 26}{space 2} 9.311826{col 37}{space 1}    0.14{col 46}{space 3}0.888{col 54}{space 4}-16.93453{col 67}{space 3} 19.56716
{txt}{space 8}360  {c |}{col 14}{res}{space 2} 1.155438{col 26}{space 2} 7.040276{col 37}{space 1}    0.16{col 46}{space 3}0.870{col 54}{space 4}-12.64325{col 67}{space 3} 14.95412
{txt}{space 8}366  {c |}{col 14}{res}{space 2} 1.403645{col 26}{space 2} 10.27968{col 37}{space 1}    0.14{col 46}{space 3}0.891{col 54}{space 4}-18.74415{col 67}{space 3} 21.55144
{txt}{space 8}367  {c |}{col 14}{res}{space 2} 1.871643{col 26}{space 2} 10.21557{col 37}{space 1}    0.18{col 46}{space 3}0.855{col 54}{space 4} -18.1505{col 67}{space 3} 21.89379
{txt}{space 8}368  {c |}{col 14}{res}{space 2}  1.86227{col 26}{space 2} 10.19406{col 37}{space 1}    0.18{col 46}{space 3}0.855{col 54}{space 4}-18.11773{col 67}{space 3} 21.84227
{txt}{space 8}375  {c |}{col 14}{res}{space 2} 2.635712{col 26}{space 2} 9.516087{col 37}{space 1}    0.28{col 46}{space 3}0.782{col 54}{space 4}-16.01548{col 67}{space 3}  21.2869
{txt}{space 8}380  {c |}{col 14}{res}{space 2}  2.39742{col 26}{space 2} 9.196749{col 37}{space 1}    0.26{col 46}{space 3}0.794{col 54}{space 4}-15.62788{col 67}{space 3} 20.42272
{txt}{space 8}390  {c |}{col 14}{res}{space 2}  1.86254{col 26}{space 2} 9.433626{col 37}{space 1}    0.20{col 46}{space 3}0.843{col 54}{space 4}-16.62703{col 67}{space 3} 20.35211
{txt}{space 12} {c |}
{space 7}party {c |}
{space 8}EPP  {c |}{col 14}{res}{space 2} 2.191257{col 26}{space 2} .9390823{col 37}{space 1}    2.33{col 46}{space 3}0.020{col 54}{space 4} .3506893{col 67}{space 3} 4.031824
{txt}{space 8}S&D  {c |}{col 14}{res}{space 2} 2.164964{col 26}{space 2} .9580595{col 37}{space 1}    2.26{col 46}{space 3}0.024{col 54}{space 4} .2872016{col 67}{space 3} 4.042726
{txt}{space 1}Greens/EFA  {c |}{col 14}{res}{space 2}-.8841029{col 26}{space 2} .9488199{col 37}{space 1}   -0.93{col 46}{space 3}0.351{col 54}{space 4}-2.743756{col 67}{space 3} .9755498
{txt}{space 2}ALDE/ADLE  {c |}{col 14}{res}{space 2}  2.35392{col 26}{space 2} .9922787{col 37}{space 1}    2.37{col 46}{space 3}0.018{col 54}{space 4}  .409089{col 67}{space 3}  4.29875
{txt}{space 8}ECR  {c |}{col 14}{res}{space 2} 4.010703{col 26}{space 2}  .989601{col 37}{space 1}    4.05{col 46}{space 3}0.000{col 54}{space 4} 2.071121{col 67}{space 3} 5.950285
{txt}{space 7}EFDD  {c |}{col 14}{res}{space 2} .0944874{col 26}{space 2} .8978138{col 37}{space 1}    0.11{col 46}{space 3}0.916{col 54}{space 4}-1.665195{col 67}{space 3}  1.85417
{txt}{space 4}GUE-NGL  {c |}{col 14}{res}{space 2}-.9853042{col 26}{space 2} 1.206767{col 37}{space 1}   -0.82{col 46}{space 3}0.414{col 54}{space 4}-3.350524{col 67}{space 3} 1.379915
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-4.736409{col 26}{space 2} 10.36941{col 37}{space 1}   -0.46{col 46}{space 3}0.648{col 54}{space 4}-25.06009{col 67}{space 3} 15.58727
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA19, title((A19))
{txt}
{com}. //Model A20
. logit prosanctions migrantshare population unemployment gdpgrowth distance col immigrantpolicy libyabill iranbill i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-649.69014}  
Iteration 2:{space 3}log pseudolikelihood = {res:-643.21539}  
Iteration 3:{space 3}log pseudolikelihood = {res:-642.86746}  
Iteration 4:{space 3}log pseudolikelihood = {res:-642.86387}  
Iteration 5:{space 3}log pseudolikelihood = {res:-642.86387}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}Wald chi2({res}16{txt}){col 67}= {res}    969.40
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-642.86387{txt}{col 49}Pseudo R2{col 67}= {res}    0.4516

{txt}{ralign 81:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}   prosanctions{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}migrantshare {c |}{col 17}{res}{space 2}-2.678541{col 29}{space 2} .4886936{col 40}{space 1}   -5.48{col 49}{space 3}0.000{col 57}{space 4}-3.636363{col 70}{space 3}-1.720719
{txt}{space 5}population {c |}{col 17}{res}{space 2}-.0065691{col 29}{space 2} .0034305{col 40}{space 1}   -1.91{col 49}{space 3}0.056{col 57}{space 4}-.0132927{col 70}{space 3} .0001545
{txt}{space 3}unemployment {c |}{col 17}{res}{space 2}-.0400731{col 29}{space 2} .0219755{col 40}{space 1}   -1.82{col 49}{space 3}0.068{col 57}{space 4}-.0831442{col 70}{space 3}  .002998
{txt}{space 6}gdpgrowth {c |}{col 17}{res}{space 2} .0542933{col 29}{space 2}  .064803{col 40}{space 1}    0.84{col 49}{space 3}0.402{col 57}{space 4}-.0727182{col 70}{space 3} .1813048
{txt}{space 7}distance {c |}{col 17}{res}{space 2} .0002083{col 29}{space 2} .0001165{col 40}{space 1}    1.79{col 49}{space 3}0.074{col 57}{space 4}  -.00002{col 70}{space 3} .0004366
{txt}{space 12}col {c |}{col 17}{res}{space 2} .0348408{col 29}{space 2} .1941812{col 40}{space 1}    0.18{col 49}{space 3}0.858{col 57}{space 4}-.3457474{col 70}{space 3}  .415429
{txt}immigrantpolicy {c |}{col 17}{res}{space 2} -.069138{col 29}{space 2} .2174125{col 40}{space 1}   -0.32{col 49}{space 3}0.750{col 57}{space 4}-.4952586{col 70}{space 3} .3569827
{txt}{space 6}libyabill {c |}{col 17}{res}{space 2} 3.972052{col 29}{space 2} .3281117{col 40}{space 1}   12.11{col 49}{space 3}0.000{col 57}{space 4} 3.328965{col 70}{space 3} 4.615139
{txt}{space 7}iranbill {c |}{col 17}{res}{space 2}  3.88882{col 29}{space 2} .3801019{col 40}{space 1}   10.23{col 49}{space 3}0.000{col 57}{space 4} 3.143834{col 70}{space 3} 4.633806
{txt}{space 15} {c |}
{space 10}party {c |}
{space 11}EPP  {c |}{col 17}{res}{space 2} 2.342075{col 29}{space 2} .9247192{col 40}{space 1}    2.53{col 49}{space 3}0.011{col 57}{space 4} .5296582{col 70}{space 3} 4.154491
{txt}{space 11}S&D  {c |}{col 17}{res}{space 2} 2.349165{col 29}{space 2} .9381271{col 40}{space 1}    2.50{col 49}{space 3}0.012{col 57}{space 4} .5104697{col 70}{space 3}  4.18786
{txt}{space 4}Greens/EFA  {c |}{col 17}{res}{space 2}-.6753978{col 29}{space 2} .9072859{col 40}{space 1}   -0.74{col 49}{space 3}0.457{col 57}{space 4}-2.453645{col 70}{space 3}  1.10285
{txt}{space 5}ALDE/ADLE  {c |}{col 17}{res}{space 2} 2.494403{col 29}{space 2} .9471015{col 40}{space 1}    2.63{col 49}{space 3}0.008{col 57}{space 4} .6381181{col 70}{space 3} 4.350688
{txt}{space 11}ECR  {c |}{col 17}{res}{space 2} 4.205224{col 29}{space 2} 1.022351{col 40}{space 1}    4.11{col 49}{space 3}0.000{col 57}{space 4} 2.201453{col 70}{space 3} 6.208994
{txt}{space 10}EFDD  {c |}{col 17}{res}{space 2} .3288589{col 29}{space 2} .9183997{col 40}{space 1}    0.36{col 49}{space 3}0.720{col 57}{space 4}-1.471171{col 70}{space 3} 2.128889
{txt}{space 7}GUE-NGL  {c |}{col 17}{res}{space 2}-.6711758{col 29}{space 2}   1.1175{col 40}{space 1}   -0.60{col 49}{space 3}0.548{col 57}{space 4}-2.861435{col 70}{space 3} 1.519084
{txt}{space 15} {c |}
{space 10}_cons {c |}{col 17}{res}{space 2} -3.83914{col 29}{space 2} 1.063799{col 40}{space 1}   -3.61{col 49}{space 3}0.000{col 57}{space 4}-5.924148{col 70}{space 3}-1.754131
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA20, title((A20))
{txt}
{com}. //Model A21
. logit prosanctions migrantshare population unemployment gdpgrowth distance col RWPvoteshare libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-629.87665}  
Iteration 2:{space 3}log pseudolikelihood = {res:-621.65081}  
Iteration 3:{space 3}log pseudolikelihood = {res:-621.25797}  
Iteration 4:{space 3}log pseudolikelihood = {res:-621.25478}  
Iteration 5:{space 3}log pseudolikelihood = {res:-621.25477}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(15)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-621.25477{txt}{col 49}Pseudo R2{col 67}= {res}    0.4700

{txt}{ralign 78:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}prosanctions{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
migrantshare {c |}{col 14}{res}{space 2}-5.542798{col 26}{space 2} 1.250855{col 37}{space 1}   -4.43{col 46}{space 3}0.000{col 54}{space 4}-7.994429{col 67}{space 3}-3.091167
{txt}{space 2}population {c |}{col 14}{res}{space 2}-.0125628{col 26}{space 2} .1306838{col 37}{space 1}   -0.10{col 46}{space 3}0.923{col 54}{space 4}-.2686983{col 67}{space 3} .2435727
{txt}unemployment {c |}{col 14}{res}{space 2}-.1016322{col 26}{space 2} .0419738{col 37}{space 1}   -2.42{col 46}{space 3}0.015{col 54}{space 4}-.1838993{col 67}{space 3}-.0193651
{txt}{space 3}gdpgrowth {c |}{col 14}{res}{space 2} .1158033{col 26}{space 2} .0838959{col 37}{space 1}    1.38{col 46}{space 3}0.167{col 54}{space 4}-.0486297{col 67}{space 3} .2802363
{txt}{space 4}distance {c |}{col 14}{res}{space 2} -.000071{col 26}{space 2} .0002088{col 37}{space 1}   -0.34{col 46}{space 3}0.734{col 54}{space 4}-.0004803{col 67}{space 3} .0003383
{txt}{space 9}col {c |}{col 14}{res}{space 2}  .031654{col 26}{space 2}  .302551{col 37}{space 1}    0.10{col 46}{space 3}0.917{col 54}{space 4} -.561335{col 67}{space 3} .6246431
{txt}RWPvoteshare {c |}{col 14}{res}{space 2}-.1117469{col 26}{space 2} .0493305{col 37}{space 1}   -2.27{col 46}{space 3}0.023{col 54}{space 4}-.2084329{col 67}{space 3} -.015061
{txt}{space 3}libyabill {c |}{col 14}{res}{space 2} 4.041334{col 26}{space 2} .4205862{col 37}{space 1}    9.61{col 46}{space 3}0.000{col 54}{space 4} 3.217001{col 67}{space 3} 4.865668
{txt}{space 4}iranbill {c |}{col 14}{res}{space 2} 4.711843{col 26}{space 2} .4932291{col 37}{space 1}    9.55{col 46}{space 3}0.000{col 54}{space 4} 3.745132{col 67}{space 3} 5.678554
{txt}{space 12} {c |}
{space 7}ccode {c |}
{space 8}205  {c |}{col 14}{res}{space 2}-1.415102{col 26}{space 2} 7.778101{col 37}{space 1}   -0.18{col 46}{space 3}0.856{col 54}{space 4} -16.6599{col 67}{space 3}  13.8297
{txt}{space 8}210  {c |}{col 14}{res}{space 2} 1.898404{col 26}{space 2} 6.207544{col 37}{space 1}    0.31{col 46}{space 3}0.760{col 54}{space 4}-10.26816{col 67}{space 3} 14.06497
{txt}{space 8}211  {c |}{col 14}{res}{space 2}-.3812161{col 26}{space 2} 6.828301{col 37}{space 1}   -0.06{col 46}{space 3}0.955{col 54}{space 4}-13.76444{col 67}{space 3} 13.00201
{txt}{space 8}212  {c |}{col 14}{res}{space 2} -1.45584{col 26}{space 2} 8.293911{col 37}{space 1}   -0.18{col 46}{space 3}0.861{col 54}{space 4}-17.71161{col 67}{space 3} 14.79993
{txt}{space 8}220  {c |}{col 14}{res}{space 2} .8027808{col 26}{space 2} .7464254{col 37}{space 1}    1.08{col 46}{space 3}0.282{col 54}{space 4}-.6601861{col 67}{space 3} 2.265748
{txt}{space 8}230  {c |}{col 14}{res}{space 2} 1.137642{col 26}{space 2} 2.436824{col 37}{space 1}    0.47{col 46}{space 3}0.641{col 54}{space 4}-3.638444{col 67}{space 3} 5.913729
{txt}{space 8}235  {c |}{col 14}{res}{space 2} .3490926{col 26}{space 2} 6.921009{col 37}{space 1}    0.05{col 46}{space 3}0.960{col 54}{space 4}-13.21584{col 67}{space 3} 13.91402
{txt}{space 8}255  {c |}{col 14}{res}{space 2} .3894127{col 26}{space 2} 2.351682{col 37}{space 1}    0.17{col 46}{space 3}0.868{col 54}{space 4}-4.219799{col 67}{space 3} 4.998624
{txt}{space 8}290  {c |}{col 14}{res}{space 2}-.5572989{col 26}{space 2} 3.421217{col 37}{space 1}   -0.16{col 46}{space 3}0.871{col 54}{space 4}-7.262761{col 67}{space 3} 6.148163
{txt}{space 8}305  {c |}{col 14}{res}{space 2} 1.856991{col 26}{space 2} 7.419606{col 37}{space 1}    0.25{col 46}{space 3}0.802{col 54}{space 4}-12.68517{col 67}{space 3} 16.39915
{txt}{space 8}310  {c |}{col 14}{res}{space 2} 1.094876{col 26}{space 2} 7.187029{col 37}{space 1}    0.15{col 46}{space 3}0.879{col 54}{space 4}-12.99144{col 67}{space 3} 15.18119
{txt}{space 8}316  {c |}{col 14}{res}{space 2} .3360811{col 26}{space 2} 6.801051{col 37}{space 1}    0.05{col 46}{space 3}0.961{col 54}{space 4}-12.99373{col 67}{space 3}  13.6659
{txt}{space 8}317  {c |}{col 14}{res}{space 2}-.0446007{col 26}{space 2} 7.710541{col 37}{space 1}   -0.01{col 46}{space 3}0.995{col 54}{space 4}-15.15698{col 67}{space 3} 15.06778
{txt}{space 8}325  {c |}{col 14}{res}{space 2} .4845007{col 26}{space 2} .7140331{col 37}{space 1}    0.68{col 46}{space 3}0.497{col 54}{space 4}-.9149784{col 67}{space 3}  1.88398
{txt}{space 8}338  {c |}{col 14}{res}{space 2}-.6017975{col 26}{space 2}  8.31648{col 37}{space 1}   -0.07{col 46}{space 3}0.942{col 54}{space 4} -16.9018{col 67}{space 3}  15.6982
{txt}{space 8}344  {c |}{col 14}{res}{space 2} 1.852608{col 26}{space 2} 7.778222{col 37}{space 1}    0.24{col 46}{space 3}0.812{col 54}{space 4}-13.39243{col 67}{space 3} 17.09764
{txt}{space 8}349  {c |}{col 14}{res}{space 2}-.6143773{col 26}{space 2} 8.004332{col 37}{space 1}   -0.08{col 46}{space 3}0.939{col 54}{space 4}-16.30258{col 67}{space 3} 15.07383
{txt}{space 8}350  {c |}{col 14}{res}{space 2} 1.270463{col 26}{space 2} 6.966121{col 37}{space 1}    0.18{col 46}{space 3}0.855{col 54}{space 4}-12.38288{col 67}{space 3} 14.92381
{txt}{space 8}352  {c |}{col 14}{res}{space 2} .5433124{col 26}{space 2} 8.039209{col 37}{space 1}    0.07{col 46}{space 3}0.946{col 54}{space 4}-15.21325{col 67}{space 3} 16.29987
{txt}{space 8}355  {c |}{col 14}{res}{space 2} .3281083{col 26}{space 2} 7.380275{col 37}{space 1}    0.04{col 46}{space 3}0.965{col 54}{space 4}-14.13697{col 67}{space 3} 14.79318
{txt}{space 8}360  {c |}{col 14}{res}{space 2}-.3325024{col 26}{space 2} 5.605028{col 37}{space 1}   -0.06{col 46}{space 3}0.953{col 54}{space 4}-11.31815{col 67}{space 3} 10.65315
{txt}{space 8}366  {c |}{col 14}{res}{space 2}-1.013936{col 26}{space 2} 8.157406{col 37}{space 1}   -0.12{col 46}{space 3}0.901{col 54}{space 4}-17.00216{col 67}{space 3} 14.97429
{txt}{space 8}367  {c |}{col 14}{res}{space 2} 1.094398{col 26}{space 2} 8.146057{col 37}{space 1}    0.13{col 46}{space 3}0.893{col 54}{space 4}-14.87158{col 67}{space 3} 17.06038
{txt}{space 8}368  {c |}{col 14}{res}{space 2}-.4476533{col 26}{space 2} 8.071398{col 37}{space 1}   -0.06{col 46}{space 3}0.956{col 54}{space 4} -16.2673{col 67}{space 3}   15.372
{txt}{space 8}375  {c |}{col 14}{res}{space 2} 1.516756{col 26}{space 2} 7.621206{col 37}{space 1}    0.20{col 46}{space 3}0.842{col 54}{space 4}-13.42053{col 67}{space 3} 16.45404
{txt}{space 8}380  {c |}{col 14}{res}{space 2} 1.361649{col 26}{space 2} 7.281459{col 37}{space 1}    0.19{col 46}{space 3}0.852{col 54}{space 4}-12.90975{col 67}{space 3} 15.63305
{txt}{space 8}390  {c |}{col 14}{res}{space 2} 1.080428{col 26}{space 2}  7.55267{col 37}{space 1}    0.14{col 46}{space 3}0.886{col 54}{space 4}-13.72253{col 67}{space 3} 15.88339
{txt}{space 12} {c |}
{space 7}party {c |}
{space 8}EPP  {c |}{col 14}{res}{space 2} 2.181032{col 26}{space 2} .9588954{col 37}{space 1}    2.27{col 46}{space 3}0.023{col 54}{space 4} .3016315{col 67}{space 3} 4.060432
{txt}{space 8}S&D  {c |}{col 14}{res}{space 2}   2.1701{col 26}{space 2} .9805872{col 37}{space 1}    2.21{col 46}{space 3}0.027{col 54}{space 4} .2481846{col 67}{space 3} 4.092016
{txt}{space 1}Greens/EFA  {c |}{col 14}{res}{space 2}-.8143244{col 26}{space 2}   .98192{col 37}{space 1}   -0.83{col 46}{space 3}0.407{col 54}{space 4}-2.738852{col 67}{space 3} 1.110203
{txt}{space 2}ALDE/ADLE  {c |}{col 14}{res}{space 2} 2.354829{col 26}{space 2} 1.006186{col 37}{space 1}    2.34{col 46}{space 3}0.019{col 54}{space 4} .3827405{col 67}{space 3} 4.326918
{txt}{space 8}ECR  {c |}{col 14}{res}{space 2}  4.02561{col 26}{space 2} .9978325{col 37}{space 1}    4.03{col 46}{space 3}0.000{col 54}{space 4} 2.069895{col 67}{space 3} 5.981326
{txt}{space 7}EFDD  {c |}{col 14}{res}{space 2} .1067189{col 26}{space 2} .9304645{col 37}{space 1}    0.11{col 46}{space 3}0.909{col 54}{space 4}-1.716958{col 67}{space 3} 1.930396
{txt}{space 4}GUE-NGL  {c |}{col 14}{res}{space 2}-.9468831{col 26}{space 2} 1.253323{col 37}{space 1}   -0.76{col 46}{space 3}0.450{col 54}{space 4}-3.403351{col 67}{space 3} 1.509585
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-2.259402{col 26}{space 2} 8.779832{col 37}{space 1}   -0.26{col 46}{space 3}0.797{col 54}{space 4}-19.46756{col 67}{space 3} 14.94875
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA21, title((A21))
{txt}
{com}. //Model A22
. logit prosanctions migrantshare population unemployment gdpgrowth distance col exportsGDP importsGDP libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1162.7147}  
Iteration 1:{space 3}log pseudolikelihood = {res:-628.03658}  
Iteration 2:{space 3}log pseudolikelihood = {res:-619.77418}  
Iteration 3:{space 3}log pseudolikelihood = {res:-619.34449}  
Iteration 4:{space 3}log pseudolikelihood = {res:-619.33916}  
Iteration 5:{space 3}log pseudolikelihood = {res:-619.33916}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,699
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(16)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-619.33916{txt}{col 49}Pseudo R2{col 67}= {res}    0.4673

{txt}{ralign 78:(Std. Err. adjusted for {res:27} clusters in ccode)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}prosanctions{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
migrantshare {c |}{col 14}{res}{space 2}-4.743758{col 26}{space 2}   1.7677{col 37}{space 1}   -2.68{col 46}{space 3}0.007{col 54}{space 4}-8.208387{col 67}{space 3}-1.279129
{txt}{space 2}population {c |}{col 14}{res}{space 2} .0746567{col 26}{space 2} .1552769{col 37}{space 1}    0.48{col 46}{space 3}0.631{col 54}{space 4}-.2296804{col 67}{space 3} .3789937
{txt}unemployment {c |}{col 14}{res}{space 2}-.0628785{col 26}{space 2}  .055378{col 37}{space 1}   -1.14{col 46}{space 3}0.256{col 54}{space 4}-.1714174{col 67}{space 3} .0456603
{txt}{space 3}gdpgrowth {c |}{col 14}{res}{space 2} .1801374{col 26}{space 2} .1026497{col 37}{space 1}    1.75{col 46}{space 3}0.079{col 54}{space 4}-.0210523{col 67}{space 3} .3813271
{txt}{space 4}distance {c |}{col 14}{res}{space 2}-.0001348{col 26}{space 2} .0002709{col 37}{space 1}   -0.50{col 46}{space 3}0.619{col 54}{space 4}-.0006657{col 67}{space 3} .0003961
{txt}{space 9}col {c |}{col 14}{res}{space 2} .6197123{col 26}{space 2} .2090283{col 37}{space 1}    2.96{col 46}{space 3}0.003{col 54}{space 4} .2100243{col 67}{space 3}   1.0294
{txt}{space 2}exportsGDP {c |}{col 14}{res}{space 2} 149.5251{col 26}{space 2}  231.972{col 37}{space 1}    0.64{col 46}{space 3}0.519{col 54}{space 4}-305.1317{col 67}{space 3} 604.1819
{txt}{space 2}importsGDP {c |}{col 14}{res}{space 2}-927.6205{col 26}{space 2} 486.3323{col 37}{space 1}   -1.91{col 46}{space 3}0.056{col 54}{space 4}-1880.814{col 67}{space 3} 25.57339
{txt}{space 3}libyabill {c |}{col 14}{res}{space 2} 4.022495{col 26}{space 2} .3732727{col 37}{space 1}   10.78{col 46}{space 3}0.000{col 54}{space 4} 3.290894{col 67}{space 3} 4.754096
{txt}{space 4}iranbill {c |}{col 14}{res}{space 2} 4.545889{col 26}{space 2} .5639129{col 37}{space 1}    8.06{col 46}{space 3}0.000{col 54}{space 4}  3.44064{col 67}{space 3} 5.651138
{txt}{space 12} {c |}
{space 7}ccode {c |}
{space 8}205  {c |}{col 14}{res}{space 2} 4.023483{col 26}{space 2}  9.40368{col 37}{space 1}    0.43{col 46}{space 3}0.669{col 54}{space 4}-14.40739{col 67}{space 3} 22.45436
{txt}{space 8}210  {c |}{col 14}{res}{space 2} 5.063843{col 26}{space 2} 7.381932{col 37}{space 1}    0.69{col 46}{space 3}0.493{col 54}{space 4}-9.404478{col 67}{space 3} 19.53216
{txt}{space 8}211  {c |}{col 14}{res}{space 2} 3.536091{col 26}{space 2} 8.133782{col 37}{space 1}    0.43{col 46}{space 3}0.664{col 54}{space 4}-12.40583{col 67}{space 3} 19.47801
{txt}{space 8}220  {c |}{col 14}{res}{space 2}-.6676991{col 26}{space 2} .3974644{col 37}{space 1}   -1.68{col 46}{space 3}0.093{col 54}{space 4}-1.446715{col 67}{space 3} .1113168
{txt}{space 8}230  {c |}{col 14}{res}{space 2} 2.300583{col 26}{space 2} 3.092743{col 37}{space 1}    0.74{col 46}{space 3}0.457{col 54}{space 4}-3.761082{col 67}{space 3} 8.362247
{txt}{space 8}235  {c |}{col 14}{res}{space 2} 4.870705{col 26}{space 2}  8.33163{col 37}{space 1}    0.58{col 46}{space 3}0.559{col 54}{space 4}-11.45899{col 67}{space 3}  21.2004
{txt}{space 8}255  {c |}{col 14}{res}{space 2}-1.306765{col 26}{space 2} 2.751185{col 37}{space 1}   -0.47{col 46}{space 3}0.635{col 54}{space 4} -6.69899{col 67}{space 3} 4.085459
{txt}{space 8}290  {c |}{col 14}{res}{space 2} 1.467405{col 26}{space 2} 4.045203{col 37}{space 1}    0.36{col 46}{space 3}0.717{col 54}{space 4}-6.461046{col 67}{space 3} 9.395856
{txt}{space 8}305  {c |}{col 14}{res}{space 2} 4.438464{col 26}{space 2} 8.686576{col 37}{space 1}    0.51{col 46}{space 3}0.609{col 54}{space 4}-12.58691{col 67}{space 3} 21.46384
{txt}{space 8}310  {c |}{col 14}{res}{space 2} 3.507778{col 26}{space 2} 8.410888{col 37}{space 1}    0.42{col 46}{space 3}0.677{col 54}{space 4}-12.97726{col 67}{space 3} 19.99282
{txt}{space 8}316  {c |}{col 14}{res}{space 2}  4.20023{col 26}{space 2} 8.082096{col 37}{space 1}    0.52{col 46}{space 3}0.603{col 54}{space 4}-11.64039{col 67}{space 3} 20.04085
{txt}{space 8}317  {c |}{col 14}{res}{space 2} 3.932732{col 26}{space 2} 9.207948{col 37}{space 1}    0.43{col 46}{space 3}0.669{col 54}{space 4}-14.11451{col 67}{space 3} 21.97998
{txt}{space 8}325  {c |}{col 14}{res}{space 2} .2324913{col 26}{space 2} .8144098{col 37}{space 1}    0.29{col 46}{space 3}0.775{col 54}{space 4}-1.363722{col 67}{space 3} 1.828705
{txt}{space 8}338  {c |}{col 14}{res}{space 2}  4.23727{col 26}{space 2} 9.898244{col 37}{space 1}    0.43{col 46}{space 3}0.669{col 54}{space 4}-15.16293{col 67}{space 3} 23.63747
{txt}{space 8}344  {c |}{col 14}{res}{space 2} 6.546239{col 26}{space 2} 9.429835{col 37}{space 1}    0.69{col 46}{space 3}0.488{col 54}{space 4} -11.9359{col 67}{space 3} 25.02838
{txt}{space 8}349  {c |}{col 14}{res}{space 2} 4.299641{col 26}{space 2} 9.510681{col 37}{space 1}    0.45{col 46}{space 3}0.651{col 54}{space 4}-14.34095{col 67}{space 3} 22.94023
{txt}{space 8}350  {c |}{col 14}{res}{space 2}  4.87444{col 26}{space 2} 8.535944{col 37}{space 1}    0.57{col 46}{space 3}0.568{col 54}{space 4} -11.8557{col 67}{space 3} 21.60458
{txt}{space 8}352  {c |}{col 14}{res}{space 2} 5.872349{col 26}{space 2} 9.642675{col 37}{space 1}    0.61{col 46}{space 3}0.543{col 54}{space 4}-13.02695{col 67}{space 3} 24.77165
{txt}{space 8}355  {c |}{col 14}{res}{space 2} 4.106307{col 26}{space 2} 8.884497{col 37}{space 1}    0.46{col 46}{space 3}0.644{col 54}{space 4}-13.30699{col 67}{space 3}  21.5196
{txt}{space 8}360  {c |}{col 14}{res}{space 2} 3.110924{col 26}{space 2} 6.664794{col 37}{space 1}    0.47{col 46}{space 3}0.641{col 54}{space 4}-9.951832{col 67}{space 3} 16.17368
{txt}{space 8}366  {c |}{col 14}{res}{space 2} 4.276914{col 26}{space 2} 9.707821{col 37}{space 1}    0.44{col 46}{space 3}0.660{col 54}{space 4}-14.75007{col 67}{space 3} 23.30389
{txt}{space 8}367  {c |}{col 14}{res}{space 2}  4.43717{col 26}{space 2} 9.662478{col 37}{space 1}    0.46{col 46}{space 3}0.646{col 54}{space 4}-14.50094{col 67}{space 3} 23.37528
{txt}{space 8}368  {c |}{col 14}{res}{space 2} 4.382814{col 26}{space 2} 9.612238{col 37}{space 1}    0.46{col 46}{space 3}0.648{col 54}{space 4}-14.45683{col 67}{space 3} 23.22245
{txt}{space 8}375  {c |}{col 14}{res}{space 2} 5.413429{col 26}{space 2} 9.051193{col 37}{space 1}    0.60{col 46}{space 3}0.550{col 54}{space 4}-12.32658{col 67}{space 3} 23.15344
{txt}{space 8}380  {c |}{col 14}{res}{space 2} 4.869461{col 26}{space 2} 8.715812{col 37}{space 1}    0.56{col 46}{space 3}0.576{col 54}{space 4}-12.21322{col 67}{space 3} 21.95214
{txt}{space 8}390  {c |}{col 14}{res}{space 2} 4.711493{col 26}{space 2} 8.980923{col 37}{space 1}    0.52{col 46}{space 3}0.600{col 54}{space 4}-12.89079{col 67}{space 3} 22.31378
{txt}{space 12} {c |}
{space 7}party {c |}
{space 8}EPP  {c |}{col 14}{res}{space 2} 2.167585{col 26}{space 2} .9566491{col 37}{space 1}    2.27{col 46}{space 3}0.023{col 54}{space 4} .2925873{col 67}{space 3} 4.042583
{txt}{space 8}S&D  {c |}{col 14}{res}{space 2} 2.140633{col 26}{space 2} .9759672{col 37}{space 1}    2.19{col 46}{space 3}0.028{col 54}{space 4} .2277725{col 67}{space 3} 4.053494
{txt}{space 1}Greens/EFA  {c |}{col 14}{res}{space 2}-.8647558{col 26}{space 2} .9703462{col 37}{space 1}   -0.89{col 46}{space 3}0.373{col 54}{space 4}-2.766599{col 67}{space 3} 1.037088
{txt}{space 2}ALDE/ADLE  {c |}{col 14}{res}{space 2} 2.271718{col 26}{space 2} 1.001702{col 37}{space 1}    2.27{col 46}{space 3}0.023{col 54}{space 4} .3084181{col 67}{space 3} 4.235018
{txt}{space 8}ECR  {c |}{col 14}{res}{space 2} 4.017167{col 26}{space 2} .9857026{col 37}{space 1}    4.08{col 46}{space 3}0.000{col 54}{space 4} 2.085225{col 67}{space 3} 5.949108
{txt}{space 7}EFDD  {c |}{col 14}{res}{space 2} .0816691{col 26}{space 2}  .925644{col 37}{space 1}    0.09{col 46}{space 3}0.930{col 54}{space 4} -1.73256{col 67}{space 3} 1.895898
{txt}{space 4}GUE-NGL  {c |}{col 14}{res}{space 2}-.9671879{col 26}{space 2}  1.22847{col 37}{space 1}   -0.79{col 46}{space 3}0.431{col 54}{space 4}-3.374944{col 67}{space 3} 1.440568
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-7.924399{col 26}{space 2} 9.997198{col 37}{space 1}   -0.79{col 46}{space 3}0.428{col 54}{space 4}-27.51855{col 67}{space 3} 11.66975
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA22, title((A22))
{txt}
{com}. //Model A23
. logit prosanctions migrantshare population unemployment gdpgrowth distance col pincometax sstax libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1045.4561}  
Iteration 1:{space 3}log pseudolikelihood = {res:-588.69359}  
Iteration 2:{space 3}log pseudolikelihood = {res:-581.95137}  
Iteration 3:{space 3}log pseudolikelihood = {res:-581.58689}  
Iteration 4:{space 3}log pseudolikelihood = {res:-581.58299}  
Iteration 5:{space 3}log pseudolikelihood = {res:-581.58299}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,522
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(16)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-581.58299{txt}{col 49}Pseudo R2{col 67}= {res}    0.4437

{txt}{ralign 78:(Std. Err. adjusted for {res:22} clusters in ccode)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}prosanctions{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
migrantshare {c |}{col 14}{res}{space 2}-6.105825{col 26}{space 2} 1.659782{col 37}{space 1}   -3.68{col 46}{space 3}0.000{col 54}{space 4}-9.358937{col 67}{space 3}-2.852713
{txt}{space 2}population {c |}{col 14}{res}{space 2}-.0591517{col 26}{space 2} .1018163{col 37}{space 1}   -0.58{col 46}{space 3}0.561{col 54}{space 4} -.258708{col 67}{space 3} .1404046
{txt}unemployment {c |}{col 14}{res}{space 2}-.0241385{col 26}{space 2} .0498878{col 37}{space 1}   -0.48{col 46}{space 3}0.628{col 54}{space 4}-.1219168{col 67}{space 3} .0736398
{txt}{space 3}gdpgrowth {c |}{col 14}{res}{space 2}-.0194923{col 26}{space 2} .1198216{col 37}{space 1}   -0.16{col 46}{space 3}0.871{col 54}{space 4}-.2543383{col 67}{space 3} .2153538
{txt}{space 4}distance {c |}{col 14}{res}{space 2}-.0004825{col 26}{space 2} .0001936{col 37}{space 1}   -2.49{col 46}{space 3}0.013{col 54}{space 4} -.000862{col 67}{space 3} -.000103
{txt}{space 9}col {c |}{col 14}{res}{space 2} .2451977{col 26}{space 2} .4272768{col 37}{space 1}    0.57{col 46}{space 3}0.566{col 54}{space 4}-.5922496{col 67}{space 3} 1.082645
{txt}{space 2}pincometax {c |}{col 14}{res}{space 2}-.4618906{col 26}{space 2} .2470891{col 37}{space 1}   -1.87{col 46}{space 3}0.062{col 54}{space 4}-.9461763{col 67}{space 3} .0223951
{txt}{space 7}sstax {c |}{col 14}{res}{space 2} .0021893{col 26}{space 2} .6070655{col 37}{space 1}    0.00{col 46}{space 3}0.997{col 54}{space 4}-1.187637{col 67}{space 3} 1.192016
{txt}{space 3}libyabill {c |}{col 14}{res}{space 2} 3.524084{col 26}{space 2} .4380298{col 37}{space 1}    8.05{col 46}{space 3}0.000{col 54}{space 4} 2.665561{col 67}{space 3} 4.382606
{txt}{space 4}iranbill {c |}{col 14}{res}{space 2}  4.87982{col 26}{space 2} .4555249{col 37}{space 1}   10.71{col 46}{space 3}0.000{col 54}{space 4} 3.987007{col 67}{space 3} 5.772632
{txt}{space 12} {c |}
{space 7}ccode {c |}
{space 8}205  {c |}{col 14}{res}{space 2} -4.35656{col 26}{space 2} 6.078456{col 37}{space 1}   -0.72{col 46}{space 3}0.474{col 54}{space 4}-16.27011{col 67}{space 3} 7.556995
{txt}{space 8}210  {c |}{col 14}{res}{space 2}-2.794546{col 26}{space 2} 7.044558{col 37}{space 1}   -0.40{col 46}{space 3}0.692{col 54}{space 4}-16.60162{col 67}{space 3} 11.01253
{txt}{space 8}211  {c |}{col 14}{res}{space 2}-2.258849{col 26}{space 2} 7.763408{col 37}{space 1}   -0.29{col 46}{space 3}0.771{col 54}{space 4}-17.47485{col 67}{space 3} 12.95715
{txt}{space 8}212  {c |}{col 14}{res}{space 2}-4.475947{col 26}{space 2} 7.527373{col 37}{space 1}   -0.59{col 46}{space 3}0.552{col 54}{space 4}-19.22933{col 67}{space 3} 10.27743
{txt}{space 8}220  {c |}{col 14}{res}{space 2}-1.461109{col 26}{space 2} 7.207634{col 37}{space 1}   -0.20{col 46}{space 3}0.839{col 54}{space 4}-15.58781{col 67}{space 3} 12.66559
{txt}{space 8}230  {c |}{col 14}{res}{space 2}-2.133197{col 26}{space 2} 3.998929{col 37}{space 1}   -0.53{col 46}{space 3}0.594{col 54}{space 4}-9.970953{col 67}{space 3} 5.704559
{txt}{space 8}235  {c |}{col 14}{res}{space 2}-3.850758{col 26}{space 2} 5.900405{col 37}{space 1}   -0.65{col 46}{space 3}0.514{col 54}{space 4}-15.41534{col 67}{space 3} 7.713824
{txt}{space 8}255  {c |}{col 14}{res}{space 2} .7292657{col 26}{space 2} 4.874443{col 37}{space 1}    0.15{col 46}{space 3}0.881{col 54}{space 4}-8.824466{col 67}{space 3}   10.283
{txt}{space 8}290  {c |}{col 14}{res}{space 2}-4.392205{col 26}{space 2} 4.628998{col 37}{space 1}   -0.95{col 46}{space 3}0.343{col 54}{space 4}-13.46487{col 67}{space 3} 4.680464
{txt}{space 8}305  {c |}{col 14}{res}{space 2} -3.55343{col 26}{space 2}  9.20067{col 37}{space 1}   -0.39{col 46}{space 3}0.699{col 54}{space 4}-21.58641{col 67}{space 3} 14.47955
{txt}{space 8}310  {c |}{col 14}{res}{space 2}-5.694924{col 26}{space 2} 7.144703{col 37}{space 1}   -0.80{col 46}{space 3}0.425{col 54}{space 4}-19.69828{col 67}{space 3} 8.308436
{txt}{space 8}316  {c |}{col 14}{res}{space 2}-6.063492{col 26}{space 2} 7.768747{col 37}{space 1}   -0.78{col 46}{space 3}0.435{col 54}{space 4}-21.28996{col 67}{space 3} 9.162972
{txt}{space 8}317  {c |}{col 14}{res}{space 2}  -7.2505{col 26}{space 2} 7.782041{col 37}{space 1}   -0.93{col 46}{space 3}0.351{col 54}{space 4}-22.50302{col 67}{space 3} 8.002019
{txt}{space 8}325  {c |}{col 14}{res}{space 2}-.3689324{col 26}{space 2} 4.421758{col 37}{space 1}   -0.08{col 46}{space 3}0.934{col 54}{space 4}-9.035419{col 67}{space 3} 8.297555
{txt}{space 8}349  {c |}{col 14}{res}{space 2}-6.333359{col 26}{space 2} 8.569233{col 37}{space 1}   -0.74{col 46}{space 3}0.460{col 54}{space 4}-23.12875{col 67}{space 3} 10.46203
{txt}{space 8}350  {c |}{col 14}{res}{space 2}-6.720208{col 26}{space 2} 6.464181{col 37}{space 1}   -1.04{col 46}{space 3}0.299{col 54}{space 4}-19.38977{col 67}{space 3} 5.949353
{txt}{space 8}366  {c |}{col 14}{res}{space 2} -5.98298{col 26}{space 2} 7.655377{col 37}{space 1}   -0.78{col 46}{space 3}0.434{col 54}{space 4}-20.98724{col 67}{space 3} 9.021284
{txt}{space 8}367  {c |}{col 14}{res}{space 2}-5.560686{col 26}{space 2} 6.727513{col 37}{space 1}   -0.83{col 46}{space 3}0.408{col 54}{space 4}-18.74637{col 67}{space 3} 7.624997
{txt}{space 8}375  {c |}{col 14}{res}{space 2}-1.147496{col 26}{space 2} 7.505014{col 37}{space 1}   -0.15{col 46}{space 3}0.878{col 54}{space 4}-15.85705{col 67}{space 3} 13.56206
{txt}{space 8}380  {c |}{col 14}{res}{space 2}-.5460644{col 26}{space 2} 8.231366{col 37}{space 1}   -0.07{col 46}{space 3}0.947{col 54}{space 4}-16.67925{col 67}{space 3} 15.58712
{txt}{space 8}390  {c |}{col 14}{res}{space 2} 4.388437{col 26}{space 2}  6.93572{col 37}{space 1}    0.63{col 46}{space 3}0.527{col 54}{space 4}-9.205325{col 67}{space 3}  17.9822
{txt}{space 12} {c |}
{space 7}party {c |}
{space 8}EPP  {c |}{col 14}{res}{space 2} 2.279238{col 26}{space 2} 1.018112{col 37}{space 1}    2.24{col 46}{space 3}0.025{col 54}{space 4} .2837745{col 67}{space 3} 4.274702
{txt}{space 8}S&D  {c |}{col 14}{res}{space 2} 2.249365{col 26}{space 2} 1.033474{col 37}{space 1}    2.18{col 46}{space 3}0.030{col 54}{space 4} .2237937{col 67}{space 3} 4.274936
{txt}{space 1}Greens/EFA  {c |}{col 14}{res}{space 2}    -.681{col 26}{space 2} 1.033612{col 37}{space 1}   -0.66{col 46}{space 3}0.510{col 54}{space 4}-2.706843{col 67}{space 3} 1.344843
{txt}{space 2}ALDE/ADLE  {c |}{col 14}{res}{space 2} 2.496281{col 26}{space 2} 1.077859{col 37}{space 1}    2.32{col 46}{space 3}0.021{col 54}{space 4} .3837155{col 67}{space 3} 4.608846
{txt}{space 8}ECR  {c |}{col 14}{res}{space 2} 4.215771{col 26}{space 2} 1.035675{col 37}{space 1}    4.07{col 46}{space 3}0.000{col 54}{space 4} 2.185885{col 67}{space 3} 6.245656
{txt}{space 7}EFDD  {c |}{col 14}{res}{space 2} .1126507{col 26}{space 2} .9535496{col 37}{space 1}    0.12{col 46}{space 3}0.906{col 54}{space 4}-1.756272{col 67}{space 3} 1.981574
{txt}{space 4}GUE-NGL  {c |}{col 14}{res}{space 2}-.8172604{col 26}{space 2} 1.322118{col 37}{space 1}   -0.62{col 46}{space 3}0.536{col 54}{space 4}-3.408563{col 67}{space 3} 1.774042
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} 6.149918{col 26}{space 2} 7.986073{col 37}{space 1}    0.77{col 46}{space 3}0.441{col 54}{space 4}-9.502497{col 67}{space 3} 21.80233
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA23, title((A23))
{txt}
{com}. //Model A24
. logit prosanctions migrantshare population unemployment gdpgrowth distance col refugees_perhomepop libyabill iranbill i.ccode i.party, cluster(ccode)

{txt}note: 349.ccode != 0 predicts success perfectly
      349.ccode dropped and 6 obs not used

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1106.6407}  
Iteration 1:{space 3}log pseudolikelihood = {res:-592.21918}  
Iteration 2:{space 3}log pseudolikelihood = {res:-583.60845}  
Iteration 3:{space 3}log pseudolikelihood = {res:-583.22621}  
Iteration 4:{space 3}log pseudolikelihood = {res:-583.22311}  
Iteration 5:{space 3}log pseudolikelihood = {res:-583.22311}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,615
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(15)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-583.22311{txt}{col 49}Pseudo R2{col 67}= {res}    0.4730

{txt}{ralign 85:(Std. Err. adjusted for {res:25} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}       prosanctions{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}migrantshare {c |}{col 21}{res}{space 2}-3.693019{col 33}{space 2} 1.812477{col 44}{space 1}   -2.04{col 53}{space 3}0.042{col 61}{space 4} -7.24541{col 74}{space 3}-.1406287
{txt}{space 9}population {c |}{col 21}{res}{space 2}-.0057238{col 33}{space 2} .1733116{col 44}{space 1}   -0.03{col 53}{space 3}0.974{col 61}{space 4}-.3454082{col 74}{space 3} .3339607
{txt}{space 7}unemployment {c |}{col 21}{res}{space 2}-.0937602{col 33}{space 2} .0653745{col 44}{space 1}   -1.43{col 53}{space 3}0.152{col 61}{space 4}-.2218918{col 74}{space 3} .0343715
{txt}{space 10}gdpgrowth {c |}{col 21}{res}{space 2} .1964695{col 33}{space 2} .1126566{col 44}{space 1}    1.74{col 53}{space 3}0.081{col 61}{space 4}-.0243334{col 74}{space 3} .4172724
{txt}{space 11}distance {c |}{col 21}{res}{space 2}-.0003294{col 33}{space 2} .0002826{col 44}{space 1}   -1.17{col 53}{space 3}0.244{col 61}{space 4}-.0008833{col 74}{space 3} .0002245
{txt}{space 16}col {c |}{col 21}{res}{space 2} .6028931{col 33}{space 2} .2066982{col 44}{space 1}    2.92{col 53}{space 3}0.004{col 61}{space 4}  .197772{col 74}{space 3} 1.008014
{txt}refugees_perhomepop {c |}{col 21}{res}{space 2}-30.77931{col 33}{space 2}  21.0134{col 44}{space 1}   -1.46{col 53}{space 3}0.143{col 61}{space 4}-71.96482{col 74}{space 3}  10.4062
{txt}{space 10}libyabill {c |}{col 21}{res}{space 2} 3.693579{col 33}{space 2} .4541855{col 44}{space 1}    8.13{col 53}{space 3}0.000{col 61}{space 4} 2.803392{col 74}{space 3} 4.583766
{txt}{space 11}iranbill {c |}{col 21}{res}{space 2} 5.240027{col 33}{space 2} .5825201{col 44}{space 1}    9.00{col 53}{space 3}0.000{col 61}{space 4} 4.098309{col 74}{space 3} 6.381745
{txt}{space 19} {c |}
{space 14}ccode {c |}
{space 15}205  {c |}{col 21}{res}{space 2}-1.063541{col 33}{space 2} 10.40591{col 44}{space 1}   -0.10{col 53}{space 3}0.919{col 61}{space 4}-21.45874{col 74}{space 3} 19.33166
{txt}{space 15}210  {c |}{col 21}{res}{space 2}  1.00096{col 33}{space 2} 8.073389{col 44}{space 1}    0.12{col 53}{space 3}0.901{col 61}{space 4}-14.82259{col 74}{space 3} 16.82451
{txt}{space 15}211  {c |}{col 21}{res}{space 2}-.8182241{col 33}{space 2} 9.103882{col 44}{space 1}   -0.09{col 53}{space 3}0.928{col 61}{space 4}-18.66151{col 74}{space 3} 17.02506
{txt}{space 15}212  {c |}{col 21}{res}{space 2}-1.321689{col 33}{space 2} 11.05088{col 44}{space 1}   -0.12{col 53}{space 3}0.905{col 61}{space 4}-22.98102{col 74}{space 3} 20.33764
{txt}{space 15}220  {c |}{col 21}{res}{space 2}-.8228492{col 33}{space 2} .4517987{col 44}{space 1}   -1.82{col 53}{space 3}0.069{col 61}{space 4}-1.708358{col 74}{space 3}   .06266
{txt}{space 15}230  {c |}{col 21}{res}{space 2} 1.122846{col 33}{space 2}   3.4187{col 44}{space 1}    0.33{col 53}{space 3}0.743{col 61}{space 4}-5.577684{col 74}{space 3} 7.823376
{txt}{space 15}235  {c |}{col 21}{res}{space 2} .4057548{col 33}{space 2} 9.258777{col 44}{space 1}    0.04{col 53}{space 3}0.965{col 61}{space 4}-17.74112{col 74}{space 3} 18.55262
{txt}{space 15}255  {c |}{col 21}{res}{space 2}-.0949222{col 33}{space 2} 3.078632{col 44}{space 1}   -0.03{col 53}{space 3}0.975{col 61}{space 4}-6.128929{col 74}{space 3} 5.939085
{txt}{space 15}290  {c |}{col 21}{res}{space 2}-.8759422{col 33}{space 2} 4.586954{col 44}{space 1}   -0.19{col 53}{space 3}0.849{col 61}{space 4}-9.866206{col 74}{space 3} 8.114322
{txt}{space 15}305  {c |}{col 21}{res}{space 2}-.5236775{col 33}{space 2} 9.571438{col 44}{space 1}   -0.05{col 53}{space 3}0.956{col 61}{space 4}-19.28335{col 74}{space 3}   18.236
{txt}{space 15}310  {c |}{col 21}{res}{space 2}-1.102289{col 33}{space 2} 9.455369{col 44}{space 1}   -0.12{col 53}{space 3}0.907{col 61}{space 4}-19.63447{col 74}{space 3}  17.4299
{txt}{space 15}316  {c |}{col 21}{res}{space 2}-.3601352{col 33}{space 2} 9.066265{col 44}{space 1}   -0.04{col 53}{space 3}0.968{col 61}{space 4}-18.12969{col 74}{space 3} 17.40942
{txt}{space 15}317  {c |}{col 21}{res}{space 2}-.9851144{col 33}{space 2} 10.38487{col 44}{space 1}   -0.09{col 53}{space 3}0.924{col 61}{space 4}-21.33909{col 74}{space 3} 19.36886
{txt}{space 15}325  {c |}{col 21}{res}{space 2}-.6666362{col 33}{space 2}  .814289{col 44}{space 1}   -0.82{col 53}{space 3}0.413{col 61}{space 4}-2.262613{col 74}{space 3} .9293409
{txt}{space 15}338  {c |}{col 21}{res}{space 2}-.3641745{col 33}{space 2} 11.16531{col 44}{space 1}   -0.03{col 53}{space 3}0.974{col 61}{space 4}-22.24779{col 74}{space 3} 21.51944
{txt}{space 15}349  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}350  {c |}{col 21}{res}{space 2}-.0663073{col 33}{space 2} 9.447175{col 44}{space 1}   -0.01{col 53}{space 3}0.994{col 61}{space 4}-18.58243{col 74}{space 3} 18.44982
{txt}{space 15}352  {c |}{col 21}{res}{space 2} .7680959{col 33}{space 2} 10.84148{col 44}{space 1}    0.07{col 53}{space 3}0.944{col 61}{space 4}-20.48082{col 74}{space 3} 22.01701
{txt}{space 15}355  {c |}{col 21}{res}{space 2}-1.027568{col 33}{space 2} 9.889488{col 44}{space 1}   -0.10{col 53}{space 3}0.917{col 61}{space 4}-20.41061{col 74}{space 3} 18.35547
{txt}{space 15}360  {c |}{col 21}{res}{space 2}-.7059544{col 33}{space 2} 7.490226{col 44}{space 1}   -0.09{col 53}{space 3}0.925{col 61}{space 4}-15.38653{col 74}{space 3} 13.97462
{txt}{space 15}367  {c |}{col 21}{res}{space 2}-1.849864{col 33}{space 2} 10.80656{col 44}{space 1}   -0.17{col 53}{space 3}0.864{col 61}{space 4}-23.03032{col 74}{space 3}  19.3306
{txt}{space 15}368  {c |}{col 21}{res}{space 2}-1.890069{col 33}{space 2} 10.77408{col 44}{space 1}   -0.18{col 53}{space 3}0.861{col 61}{space 4}-23.00688{col 74}{space 3} 19.22674
{txt}{space 15}375  {c |}{col 21}{res}{space 2}  .707022{col 33}{space 2} 10.08624{col 44}{space 1}    0.07{col 53}{space 3}0.944{col 61}{space 4}-19.06165{col 74}{space 3} 20.47569
{txt}{space 15}380  {c |}{col 21}{res}{space 2} .5247556{col 33}{space 2} 9.641809{col 44}{space 1}    0.05{col 53}{space 3}0.957{col 61}{space 4}-18.37284{col 74}{space 3} 19.42235
{txt}{space 15}390  {c |}{col 21}{res}{space 2} .2576343{col 33}{space 2} 10.03944{col 44}{space 1}    0.03{col 53}{space 3}0.980{col 61}{space 4} -19.4193{col 74}{space 3} 19.93457
{txt}{space 19} {c |}
{space 14}party {c |}
{space 15}EPP  {c |}{col 21}{res}{space 2} 2.184419{col 33}{space 2} .9626895{col 44}{space 1}    2.27{col 53}{space 3}0.023{col 61}{space 4} .2975826{col 74}{space 3} 4.071256
{txt}{space 15}S&D  {c |}{col 21}{res}{space 2} 2.106227{col 33}{space 2} .9885517{col 44}{space 1}    2.13{col 53}{space 3}0.033{col 61}{space 4} .1687017{col 74}{space 3} 4.043753
{txt}{space 8}Greens/EFA  {c |}{col 21}{res}{space 2}-1.006637{col 33}{space 2} .9735156{col 44}{space 1}   -1.03{col 53}{space 3}0.301{col 61}{space 4}-2.914693{col 74}{space 3} .9014184
{txt}{space 9}ALDE/ADLE  {c |}{col 21}{res}{space 2} 2.249171{col 33}{space 2} 1.019696{col 44}{space 1}    2.21{col 53}{space 3}0.027{col 61}{space 4} .2506041{col 74}{space 3} 4.247738
{txt}{space 15}ECR  {c |}{col 21}{res}{space 2} 3.960936{col 33}{space 2} 1.010157{col 44}{space 1}    3.92{col 53}{space 3}0.000{col 61}{space 4} 1.981064{col 74}{space 3} 5.940808
{txt}{space 14}EFDD  {c |}{col 21}{res}{space 2}-.0866679{col 33}{space 2} .8989327{col 44}{space 1}   -0.10{col 53}{space 3}0.923{col 61}{space 4}-1.848544{col 74}{space 3} 1.675208
{txt}{space 11}GUE-NGL  {c |}{col 21}{res}{space 2}-1.287947{col 33}{space 2} 1.274736{col 44}{space 1}   -1.01{col 53}{space 3}0.312{col 61}{space 4}-3.786384{col 74}{space 3}  1.21049
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} -1.83988{col 33}{space 2} 10.96196{col 44}{space 1}   -0.17{col 53}{space 3}0.867{col 61}{space 4}-23.32492{col 74}{space 3} 19.64516
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA24, title((A24))
{txt}
{com}. //Model A25
. logit prosanctions migrantshare population unemployment gdpgrowth distance col lnremit libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1087.5167}  
Iteration 1:{space 3}log pseudolikelihood = {res:-607.63498}  
Iteration 2:{space 3}log pseudolikelihood = {res:-600.42243}  
Iteration 3:{space 3}log pseudolikelihood = {res:-600.10331}  
Iteration 4:{space 3}log pseudolikelihood = {res:-600.10073}  
Iteration 5:{space 3}log pseudolikelihood = {res:-600.10072}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,576
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(15)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-600.10072{txt}{col 49}Pseudo R2{col 67}= {res}    0.4482

{txt}{ralign 78:(Std. Err. adjusted for {res:27} clusters in ccode)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}prosanctions{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
migrantshare {c |}{col 14}{res}{space 2}-5.476343{col 26}{space 2} 1.511712{col 37}{space 1}   -3.62{col 46}{space 3}0.000{col 54}{space 4}-8.439243{col 67}{space 3}-2.513442
{txt}{space 2}population {c |}{col 14}{res}{space 2} .1208845{col 26}{space 2} .1694463{col 37}{space 1}    0.71{col 46}{space 3}0.476{col 54}{space 4}-.2112241{col 67}{space 3} .4529931
{txt}unemployment {c |}{col 14}{res}{space 2}-.0996855{col 26}{space 2} .0688857{col 37}{space 1}   -1.45{col 46}{space 3}0.148{col 54}{space 4}-.2346991{col 67}{space 3} .0353281
{txt}{space 3}gdpgrowth {c |}{col 14}{res}{space 2} .1303668{col 26}{space 2} .1139082{col 37}{space 1}    1.14{col 46}{space 3}0.252{col 54}{space 4} -.092889{col 67}{space 3} .3536227
{txt}{space 4}distance {c |}{col 14}{res}{space 2}-.0003729{col 26}{space 2} .0002633{col 37}{space 1}   -1.42{col 46}{space 3}0.157{col 54}{space 4}-.0008889{col 67}{space 3}  .000143
{txt}{space 9}col {c |}{col 14}{res}{space 2} .6771452{col 26}{space 2} .2020419{col 37}{space 1}    3.35{col 46}{space 3}0.001{col 54}{space 4} .2811504{col 67}{space 3}  1.07314
{txt}{space 5}lnremit {c |}{col 14}{res}{space 2}-.1933375{col 26}{space 2} .1787167{col 37}{space 1}   -1.08{col 46}{space 3}0.279{col 54}{space 4}-.5436158{col 67}{space 3} .1569409
{txt}{space 3}libyabill {c |}{col 14}{res}{space 2} 3.278379{col 26}{space 2} .4835719{col 37}{space 1}    6.78{col 46}{space 3}0.000{col 54}{space 4} 2.330596{col 67}{space 3} 4.226163
{txt}{space 4}iranbill {c |}{col 14}{res}{space 2} 4.869694{col 26}{space 2} .4745923{col 37}{space 1}   10.26{col 46}{space 3}0.000{col 54}{space 4} 3.939511{col 67}{space 3} 5.799878
{txt}{space 12} {c |}
{space 7}ccode {c |}
{space 8}205  {c |}{col 14}{res}{space 2} 6.669918{col 26}{space 2} 10.06336{col 37}{space 1}    0.66{col 46}{space 3}0.507{col 54}{space 4}-13.05391{col 67}{space 3} 26.39374
{txt}{space 8}210  {c |}{col 14}{res}{space 2} 6.619866{col 26}{space 2} 7.858359{col 37}{space 1}    0.84{col 46}{space 3}0.400{col 54}{space 4}-8.782235{col 67}{space 3} 22.02197
{txt}{space 8}211  {c |}{col 14}{res}{space 2} 5.866409{col 26}{space 2} 8.890342{col 37}{space 1}    0.66{col 46}{space 3}0.509{col 54}{space 4}-11.55834{col 67}{space 3} 23.29116
{txt}{space 8}212  {c |}{col 14}{res}{space 2} 6.106006{col 26}{space 2} 10.64593{col 37}{space 1}    0.57{col 46}{space 3}0.566{col 54}{space 4}-14.75963{col 67}{space 3} 26.97164
{txt}{space 8}220  {c |}{col 14}{res}{space 2}-.8306309{col 26}{space 2} .4554409{col 37}{space 1}   -1.82{col 46}{space 3}0.068{col 54}{space 4}-1.723279{col 67}{space 3} .0620169
{txt}{space 8}230  {c |}{col 14}{res}{space 2} 3.273851{col 26}{space 2} 3.566523{col 37}{space 1}    0.92{col 46}{space 3}0.359{col 54}{space 4}-3.716406{col 67}{space 3} 10.26411
{txt}{space 8}235  {c |}{col 14}{res}{space 2} 6.942887{col 26}{space 2} 8.990777{col 37}{space 1}    0.77{col 46}{space 3}0.440{col 54}{space 4}-10.67871{col 67}{space 3} 24.56449
{txt}{space 8}255  {c |}{col 14}{res}{space 2}-2.147164{col 26}{space 2} 2.961752{col 37}{space 1}   -0.72{col 46}{space 3}0.468{col 54}{space 4}-7.952091{col 67}{space 3} 3.657762
{txt}{space 8}290  {c |}{col 14}{res}{space 2} 2.210312{col 26}{space 2} 4.434796{col 37}{space 1}    0.50{col 46}{space 3}0.618{col 54}{space 4}-6.481729{col 67}{space 3} 10.90235
{txt}{space 8}305  {c |}{col 14}{res}{space 2} 6.033585{col 26}{space 2} 9.246424{col 37}{space 1}    0.65{col 46}{space 3}0.514{col 54}{space 4}-12.08907{col 67}{space 3} 24.15624
{txt}{space 8}310  {c |}{col 14}{res}{space 2} 5.607845{col 26}{space 2} 9.187282{col 37}{space 1}    0.61{col 46}{space 3}0.542{col 54}{space 4} -12.3989{col 67}{space 3} 23.61459
{txt}{space 8}316  {c |}{col 14}{res}{space 2} 5.735822{col 26}{space 2}   8.7369{col 37}{space 1}    0.66{col 46}{space 3}0.511{col 54}{space 4}-11.38819{col 67}{space 3} 22.85983
{txt}{space 8}317  {c |}{col 14}{res}{space 2} 5.978685{col 26}{space 2} 10.15154{col 37}{space 1}    0.59{col 46}{space 3}0.556{col 54}{space 4}-13.91796{col 67}{space 3} 25.87533
{txt}{space 8}325  {c |}{col 14}{res}{space 2}-.1714272{col 26}{space 2} .8285893{col 37}{space 1}   -0.21{col 46}{space 3}0.836{col 54}{space 4}-1.795432{col 67}{space 3} 1.452578
{txt}{space 8}338  {c |}{col 14}{res}{space 2} 5.605926{col 26}{space 2} 10.64503{col 37}{space 1}    0.53{col 46}{space 3}0.598{col 54}{space 4}-15.25795{col 67}{space 3}  26.4698
{txt}{space 8}349  {c |}{col 14}{res}{space 2} 6.089263{col 26}{space 2} 10.22771{col 37}{space 1}    0.60{col 46}{space 3}0.552{col 54}{space 4}-13.95669{col 67}{space 3} 26.13521
{txt}{space 8}350  {c |}{col 14}{res}{space 2} 6.265355{col 26}{space 2} 9.418466{col 37}{space 1}    0.67{col 46}{space 3}0.506{col 54}{space 4} -12.1945{col 67}{space 3} 24.72521
{txt}{space 8}352  {c |}{col 14}{res}{space 2} 7.934712{col 26}{space 2} 10.61232{col 37}{space 1}    0.75{col 46}{space 3}0.455{col 54}{space 4}-12.86506{col 67}{space 3} 28.73448
{txt}{space 8}355  {c |}{col 14}{res}{space 2} 4.765552{col 26}{space 2} 9.596178{col 37}{space 1}    0.50{col 46}{space 3}0.619{col 54}{space 4}-14.04261{col 67}{space 3} 23.57371
{txt}{space 8}360  {c |}{col 14}{res}{space 2} 4.650902{col 26}{space 2} 7.284716{col 37}{space 1}    0.64{col 46}{space 3}0.523{col 54}{space 4}-9.626879{col 67}{space 3} 18.92868
{txt}{space 8}366  {c |}{col 14}{res}{space 2} 6.244416{col 26}{space 2} 10.62881{col 37}{space 1}    0.59{col 46}{space 3}0.557{col 54}{space 4}-14.58767{col 67}{space 3}  27.0765
{txt}{space 8}367  {c |}{col 14}{res}{space 2} 6.798711{col 26}{space 2} 10.45892{col 37}{space 1}    0.65{col 46}{space 3}0.516{col 54}{space 4} -13.7004{col 67}{space 3} 27.29782
{txt}{space 8}368  {c |}{col 14}{res}{space 2} 5.612408{col 26}{space 2} 10.49109{col 37}{space 1}    0.53{col 46}{space 3}0.593{col 54}{space 4}-14.94974{col 67}{space 3} 26.17456
{txt}{space 8}375  {c |}{col 14}{res}{space 2} 7.695838{col 26}{space 2} 9.793278{col 37}{space 1}    0.79{col 46}{space 3}0.432{col 54}{space 4}-11.49864{col 67}{space 3} 26.89031
{txt}{space 8}380  {c |}{col 14}{res}{space 2} 7.770999{col 26}{space 2} 9.623303{col 37}{space 1}    0.81{col 46}{space 3}0.419{col 54}{space 4}-11.09033{col 67}{space 3} 26.63233
{txt}{space 8}390  {c |}{col 14}{res}{space 2} 7.128574{col 26}{space 2} 9.766306{col 37}{space 1}    0.73{col 46}{space 3}0.465{col 54}{space 4}-12.01303{col 67}{space 3} 26.27018
{txt}{space 12} {c |}
{space 7}party {c |}
{space 8}EPP  {c |}{col 14}{res}{space 2} 1.968924{col 26}{space 2} .9947489{col 37}{space 1}    1.98{col 46}{space 3}0.048{col 54}{space 4} .0192523{col 67}{space 3} 3.918596
{txt}{space 8}S&D  {c |}{col 14}{res}{space 2} 1.981865{col 26}{space 2} 1.010753{col 37}{space 1}    1.96{col 46}{space 3}0.050{col 54}{space 4} .0008255{col 67}{space 3} 3.962904
{txt}{space 1}Greens/EFA  {c |}{col 14}{res}{space 2}-.9192357{col 26}{space 2}  .979495{col 37}{space 1}   -0.94{col 46}{space 3}0.348{col 54}{space 4}-2.839011{col 67}{space 3} 1.000539
{txt}{space 2}ALDE/ADLE  {c |}{col 14}{res}{space 2} 2.142579{col 26}{space 2} 1.025767{col 37}{space 1}    2.09{col 46}{space 3}0.037{col 54}{space 4} .1321133{col 67}{space 3} 4.153044
{txt}{space 8}ECR  {c |}{col 14}{res}{space 2} 3.845436{col 26}{space 2}  1.00848{col 37}{space 1}    3.81{col 46}{space 3}0.000{col 54}{space 4} 1.868851{col 67}{space 3} 5.822021
{txt}{space 7}EFDD  {c |}{col 14}{res}{space 2}-.1741449{col 26}{space 2}  .922249{col 37}{space 1}   -0.19{col 46}{space 3}0.850{col 54}{space 4} -1.98172{col 67}{space 3}  1.63343
{txt}{space 4}GUE-NGL  {c |}{col 14}{res}{space 2}-.8584325{col 26}{space 2} 1.275911{col 37}{space 1}   -0.67{col 46}{space 3}0.501{col 54}{space 4}-3.359172{col 67}{space 3} 1.642307
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} -8.85599{col 26}{space 2} 10.47153{col 37}{space 1}   -0.85{col 46}{space 3}0.398{col 54}{space 4}-29.37981{col 67}{space 3} 11.66783
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA25, title((A25))
{txt}
{com}. 
. estout MODELA15 MODELA16 MODELA17 MODELA18 MODELA19 MODELA20 MODELA21 MODELA22 MODELA23 MODELA24 MODELA25, cells(b(star fmt(3)) se(par fmt(3))) starlevels($^{c -(}+{c )-}$ 0.10 $^{c -(}*{c )-}$ 0.05 $^{c -(}**{c )-}$ 0.01 $^{c -(}***{c )-}$ 0.001) stats(N N_g r2, fmt(0 0 3) label(Observations Countries R$^2$)) label,  using "modelsa15_a25.tex", replace style(tex)
{res}{txt}(note: file modelsa15_a25.tex not found)
(output written to {browse  `"modelsa15_a25.tex"'})

{com}. 
. **TABLE A5
. //Model A26
. logit prosanctions migrantshare2, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1161.8867}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1161.8832}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1161.8832}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      2.62
{txt}{col 49}Prob > chi2{col 67}= {res}    0.1058
{txt}Log pseudolikelihood = {res}-1161.8832{txt}{col 49}Pseudo R2{col 67}= {res}    0.0089

{txt}{ralign 79:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1} prosanctions{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
migrantshare2 {c |}{col 15}{res}{space 2}-.2569832{col 27}{space 2} .1588914{col 38}{space 1}   -1.62{col 47}{space 3}0.106{col 55}{space 4}-.5684047{col 68}{space 3} .0544383
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} .3837212{col 27}{space 2} .1460742{col 38}{space 1}    2.63{col 47}{space 3}0.009{col 55}{space 4}  .097421{col 68}{space 3} .6700214
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA26, title((A26))
{txt}
{com}. //Model A27
. logit prosanctions migrantshare2 libyabill iranbill, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-830.10121}  
Iteration 2:{space 3}log pseudolikelihood = {res:-828.21311}  
Iteration 3:{space 3}log pseudolikelihood = {res:-828.20984}  
Iteration 4:{space 3}log pseudolikelihood = {res:-828.20984}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}Wald chi2({res}3{txt}){col 67}= {res}    140.87
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-828.20984{txt}{col 49}Pseudo R2{col 67}= {res}    0.2935

{txt}{ralign 79:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1} prosanctions{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
migrantshare2 {c |}{col 15}{res}{space 2}-.2778954{col 27}{space 2}  .096968{col 38}{space 1}   -2.87{col 47}{space 3}0.004{col 55}{space 4}-.4679491{col 68}{space 3}-.0878416
{txt}{space 4}libyabill {c |}{col 15}{res}{space 2} 2.964641{col 27}{space 2} .2549269{col 38}{space 1}   11.63{col 47}{space 3}0.000{col 55}{space 4} 2.464993{col 68}{space 3} 3.464288
{txt}{space 5}iranbill {c |}{col 15}{res}{space 2} 3.263188{col 27}{space 2} .3330391{col 38}{space 1}    9.80{col 47}{space 3}0.000{col 55}{space 4} 2.610443{col 68}{space 3} 3.915933
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} -1.73501{col 27}{space 2} .1658738{col 38}{space 1}  -10.46{col 47}{space 3}0.000{col 55}{space 4}-2.060117{col 68}{space 3}-1.409903
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA27, title((A27))
{txt}
{com}. //Model A28
. logit prosanctions migrantshare2 libyabill iranbill i.ccode, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-785.18934}  
Iteration 2:{space 3}log pseudolikelihood = {res:-780.13884}  
Iteration 3:{space 3}log pseudolikelihood = {res:  -780.087}  
Iteration 4:{space 3}log pseudolikelihood = {res:-780.08694}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(3)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-780.08694{txt}{col 49}Pseudo R2{col 67}= {res}    0.3346

{txt}{ralign 79:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1} prosanctions{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
migrantshare2 {c |}{col 15}{res}{space 2}-.5729887{col 27}{space 2} .1686998{col 38}{space 1}   -3.40{col 47}{space 3}0.001{col 55}{space 4}-.9036342{col 68}{space 3}-.2423432
{txt}{space 4}libyabill {c |}{col 15}{res}{space 2} 3.028151{col 27}{space 2} .2612759{col 38}{space 1}   11.59{col 47}{space 3}0.000{col 55}{space 4} 2.516059{col 68}{space 3} 3.540242
{txt}{space 5}iranbill {c |}{col 15}{res}{space 2} 3.462357{col 27}{space 2} .3332675{col 38}{space 1}   10.39{col 47}{space 3}0.000{col 55}{space 4} 2.809164{col 68}{space 3} 4.115549
{txt}{space 13} {c |}
{space 8}ccode {c |}
{space 9}205  {c |}{col 15}{res}{space 2}-1.598712{col 27}{space 2} .0889594{col 38}{space 1}  -17.97{col 47}{space 3}0.000{col 55}{space 4}-1.773069{col 68}{space 3}-1.424354
{txt}{space 9}210  {c |}{col 15}{res}{space 2} .0276528{col 27}{space 2} .0437827{col 38}{space 1}    0.63{col 47}{space 3}0.528{col 55}{space 4}-.0581598{col 68}{space 3} .1134653
{txt}{space 9}211  {c |}{col 15}{res}{space 2}-.7198204{col 27}{space 2} .0692242{col 38}{space 1}  -10.40{col 47}{space 3}0.000{col 55}{space 4}-.8554973{col 68}{space 3}-.5841435
{txt}{space 9}212  {c |}{col 15}{res}{space 2} -.598749{col 27}{space 2} .0751985{col 38}{space 1}   -7.96{col 47}{space 3}0.000{col 55}{space 4}-.7461353{col 68}{space 3}-.4513627
{txt}{space 9}220  {c |}{col 15}{res}{space 2}-1.566819{col 27}{space 2} .0886767{col 38}{space 1}  -17.67{col 47}{space 3}0.000{col 55}{space 4}-1.740622{col 68}{space 3}-1.393016
{txt}{space 9}230  {c |}{col 15}{res}{space 2}-.8725995{col 27}{space 2} .0811214{col 38}{space 1}  -10.76{col 47}{space 3}0.000{col 55}{space 4}-1.031595{col 68}{space 3}-.7136044
{txt}{space 9}235  {c |}{col 15}{res}{space 2}-.3221694{col 27}{space 2} .0771866{col 38}{space 1}   -4.17{col 47}{space 3}0.000{col 55}{space 4}-.4734523{col 68}{space 3}-.1708864
{txt}{space 9}255  {c |}{col 15}{res}{space 2}-.5815157{col 27}{space 2} .0123808{col 38}{space 1}  -46.97{col 47}{space 3}0.000{col 55}{space 4}-.6057815{col 68}{space 3}-.5572498
{txt}{space 9}290  {c |}{col 15}{res}{space 2}-.0824632{col 27}{space 2} .0689971{col 38}{space 1}   -1.20{col 47}{space 3}0.232{col 55}{space 4}-.2176949{col 68}{space 3} .0527686
{txt}{space 9}305  {c |}{col 15}{res}{space 2}-.8924538{col 27}{space 2} .0309725{col 38}{space 1}  -28.81{col 47}{space 3}0.000{col 55}{space 4}-.9531588{col 68}{space 3}-.8317488
{txt}{space 9}310  {c |}{col 15}{res}{space 2}-.5150646{col 27}{space 2}  .040444{col 38}{space 1}  -12.74{col 47}{space 3}0.000{col 55}{space 4}-.5943333{col 68}{space 3}-.4357958
{txt}{space 9}316  {c |}{col 15}{res}{space 2}-.0143286{col 27}{space 2} .0667621{col 38}{space 1}   -0.21{col 47}{space 3}0.830{col 55}{space 4}  -.14518{col 68}{space 3} .1165227
{txt}{space 9}317  {c |}{col 15}{res}{space 2}-.2978434{col 27}{space 2} .0705648{col 38}{space 1}   -4.22{col 47}{space 3}0.000{col 55}{space 4}-.4361478{col 68}{space 3} -.159539
{txt}{space 9}325  {c |}{col 15}{res}{space 2}-.8642498{col 27}{space 2}  .027388{col 38}{space 1}  -31.56{col 47}{space 3}0.000{col 55}{space 4}-.9179293{col 68}{space 3}-.8105703
{txt}{space 9}338  {c |}{col 15}{res}{space 2} 1.129913{col 27}{space 2} .3255432{col 38}{space 1}    3.47{col 47}{space 3}0.001{col 55}{space 4} .4918605{col 68}{space 3} 1.767966
{txt}{space 9}344  {c |}{col 15}{res}{space 2} .8540563{col 27}{space 2} .1259997{col 38}{space 1}    6.78{col 47}{space 3}0.000{col 55}{space 4} .6071015{col 68}{space 3} 1.101011
{txt}{space 9}349  {c |}{col 15}{res}{space 2}-.3090256{col 27}{space 2} .0755437{col 38}{space 1}   -4.09{col 47}{space 3}0.000{col 55}{space 4}-.4570886{col 68}{space 3}-.1609625
{txt}{space 9}350  {c |}{col 15}{res}{space 2}-2.169022{col 27}{space 2} .0822188{col 38}{space 1}  -26.38{col 47}{space 3}0.000{col 55}{space 4}-2.330168{col 68}{space 3}-2.007876
{txt}{space 9}352  {c |}{col 15}{res}{space 2}-.7395052{col 27}{space 2} .0399387{col 38}{space 1}  -18.52{col 47}{space 3}0.000{col 55}{space 4}-.8177836{col 68}{space 3}-.6612269
{txt}{space 9}355  {c |}{col 15}{res}{space 2} .0208999{col 27}{space 2} .0454318{col 38}{space 1}    0.46{col 47}{space 3}0.645{col 55}{space 4}-.0681448{col 68}{space 3} .1099446
{txt}{space 9}360  {c |}{col 15}{res}{space 2} 1.572513{col 27}{space 2} .4984486{col 38}{space 1}    3.15{col 47}{space 3}0.002{col 55}{space 4} .5955719{col 68}{space 3} 2.549454
{txt}{space 9}366  {c |}{col 15}{res}{space 2}-.5822004{col 27}{space 2} .0783365{col 38}{space 1}   -7.43{col 47}{space 3}0.000{col 55}{space 4}-.7357372{col 68}{space 3}-.4286636
{txt}{space 9}367  {c |}{col 15}{res}{space 2}-.6781894{col 27}{space 2}  .095797{col 38}{space 1}   -7.08{col 47}{space 3}0.000{col 55}{space 4}-.8659482{col 68}{space 3}-.4904307
{txt}{space 9}368  {c |}{col 15}{res}{space 2}-.3023193{col 27}{space 2} .0771518{col 38}{space 1}   -3.92{col 47}{space 3}0.000{col 55}{space 4}-.4535341{col 68}{space 3}-.1511046
{txt}{space 9}375  {c |}{col 15}{res}{space 2} .4259301{col 27}{space 2} .0936643{col 38}{space 1}    4.55{col 47}{space 3}0.000{col 55}{space 4} .2423514{col 68}{space 3} .6095088
{txt}{space 9}380  {c |}{col 15}{res}{space 2} .2406293{col 27}{space 2} .4271683{col 38}{space 1}    0.56{col 47}{space 3}0.573{col 55}{space 4}-.5966052{col 68}{space 3} 1.077864
{txt}{space 9}390  {c |}{col 15}{res}{space 2}-.0495053{col 27}{space 2} .1322098{col 38}{space 1}   -0.37{col 47}{space 3}0.708{col 55}{space 4}-.3086316{col 68}{space 3} .2096211
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2}-1.154576{col 27}{space 2}  .191267{col 38}{space 1}   -6.04{col 47}{space 3}0.000{col 55}{space 4}-1.529453{col 68}{space 3}-.7796996
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA28, title((A28))
{txt}
{com}. //Model A29
. logit prosanctions migrantshare2 libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res: -635.1404}  
Iteration 2:{space 3}log pseudolikelihood = {res:-626.15894}  
Iteration 3:{space 3}log pseudolikelihood = {res: -625.7669}  
Iteration 4:{space 3}log pseudolikelihood = {res:-625.76402}  
Iteration 5:{space 3}log pseudolikelihood = {res:-625.76402}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(9)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-625.76402{txt}{col 49}Pseudo R2{col 67}= {res}    0.4662

{txt}{ralign 79:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1} prosanctions{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
migrantshare2 {c |}{col 15}{res}{space 2}-.5720346{col 27}{space 2} .1236359{col 38}{space 1}   -4.63{col 47}{space 3}0.000{col 55}{space 4}-.8143564{col 68}{space 3}-.3297127
{txt}{space 4}libyabill {c |}{col 15}{res}{space 2} 3.716925{col 27}{space 2} .3149868{col 38}{space 1}   11.80{col 47}{space 3}0.000{col 55}{space 4} 3.099562{col 68}{space 3} 4.334288
{txt}{space 5}iranbill {c |}{col 15}{res}{space 2} 4.021755{col 27}{space 2} .3581443{col 38}{space 1}   11.23{col 47}{space 3}0.000{col 55}{space 4} 3.319805{col 68}{space 3} 4.723705
{txt}{space 13} {c |}
{space 8}ccode {c |}
{space 9}205  {c |}{col 15}{res}{space 2}-1.037508{col 27}{space 2} .0943721{col 38}{space 1}  -10.99{col 47}{space 3}0.000{col 55}{space 4}-1.222474{col 68}{space 3}-.8525418
{txt}{space 9}210  {c |}{col 15}{res}{space 2} 1.045039{col 27}{space 2}  .177368{col 38}{space 1}    5.89{col 47}{space 3}0.000{col 55}{space 4} .6974044{col 68}{space 3} 1.392674
{txt}{space 9}211  {c |}{col 15}{res}{space 2}-.5396131{col 27}{space 2} .0722255{col 38}{space 1}   -7.47{col 47}{space 3}0.000{col 55}{space 4}-.6811725{col 68}{space 3}-.3980536
{txt}{space 9}212  {c |}{col 15}{res}{space 2}-.2128751{col 27}{space 2} .0834904{col 38}{space 1}   -2.55{col 47}{space 3}0.011{col 55}{space 4}-.3765132{col 68}{space 3}-.0492369
{txt}{space 9}220  {c |}{col 15}{res}{space 2}-.8868756{col 27}{space 2} .1880112{col 38}{space 1}   -4.72{col 47}{space 3}0.000{col 55}{space 4}-1.255371{col 68}{space 3}-.5183805
{txt}{space 9}230  {c |}{col 15}{res}{space 2}-.5055304{col 27}{space 2} .1221417{col 38}{space 1}   -4.14{col 47}{space 3}0.000{col 55}{space 4}-.7449238{col 68}{space 3}-.2661371
{txt}{space 9}235  {c |}{col 15}{res}{space 2} .1173613{col 27}{space 2} .1536438{col 38}{space 1}    0.76{col 47}{space 3}0.445{col 55}{space 4} -.183775{col 68}{space 3} .4184975
{txt}{space 9}255  {c |}{col 15}{res}{space 2}-.0206843{col 27}{space 2}  .098637{col 38}{space 1}   -0.21{col 47}{space 3}0.834{col 55}{space 4}-.2140093{col 68}{space 3} .1726406
{txt}{space 9}290  {c |}{col 15}{res}{space 2}-.3316768{col 27}{space 2} .1360975{col 38}{space 1}   -2.44{col 47}{space 3}0.015{col 55}{space 4}-.5984231{col 68}{space 3}-.0649306
{txt}{space 9}305  {c |}{col 15}{res}{space 2}-.1968089{col 27}{space 2} .2579066{col 38}{space 1}   -0.76{col 47}{space 3}0.445{col 55}{space 4}-.7022965{col 68}{space 3} .3086787
{txt}{space 9}310  {c |}{col 15}{res}{space 2}-.3229303{col 27}{space 2} .1269255{col 38}{space 1}   -2.54{col 47}{space 3}0.011{col 55}{space 4}-.5716996{col 68}{space 3}-.0741609
{txt}{space 9}316  {c |}{col 15}{res}{space 2} .2341589{col 27}{space 2} .1672291{col 38}{space 1}    1.40{col 47}{space 3}0.161{col 55}{space 4} -.093604{col 68}{space 3} .5619219
{txt}{space 9}317  {c |}{col 15}{res}{space 2}-.5088863{col 27}{space 2}  .110663{col 38}{space 1}   -4.60{col 47}{space 3}0.000{col 55}{space 4}-.7257819{col 68}{space 3}-.2919908
{txt}{space 9}325  {c |}{col 15}{res}{space 2}-.6066592{col 27}{space 2} .1391677{col 38}{space 1}   -4.36{col 47}{space 3}0.000{col 55}{space 4}-.8794228{col 68}{space 3}-.3338956
{txt}{space 9}338  {c |}{col 15}{res}{space 2} .9133497{col 27}{space 2} .2594801{col 38}{space 1}    3.52{col 47}{space 3}0.000{col 55}{space 4} .4047779{col 68}{space 3} 1.421921
{txt}{space 9}344  {c |}{col 15}{res}{space 2} .8169772{col 27}{space 2} .1334578{col 38}{space 1}    6.12{col 47}{space 3}0.000{col 55}{space 4} .5554048{col 68}{space 3}  1.07855
{txt}{space 9}349  {c |}{col 15}{res}{space 2}-.3496766{col 27}{space 2} .0841868{col 38}{space 1}   -4.15{col 47}{space 3}0.000{col 55}{space 4}-.5146797{col 68}{space 3}-.1846735
{txt}{space 9}350  {c |}{col 15}{res}{space 2}-1.296664{col 27}{space 2} .1332457{col 38}{space 1}   -9.73{col 47}{space 3}0.000{col 55}{space 4} -1.55782{col 68}{space 3}-1.035507
{txt}{space 9}352  {c |}{col 15}{res}{space 2} .1813151{col 27}{space 2} .2564164{col 38}{space 1}    0.71{col 47}{space 3}0.479{col 55}{space 4}-.3212517{col 68}{space 3}  .683882
{txt}{space 9}355  {c |}{col 15}{res}{space 2}-.2324387{col 27}{space 2} .0795195{col 38}{space 1}   -2.92{col 47}{space 3}0.003{col 55}{space 4} -.388294{col 68}{space 3}-.0765834
{txt}{space 9}360  {c |}{col 15}{res}{space 2} 1.506218{col 27}{space 2} .4076808{col 38}{space 1}    3.69{col 47}{space 3}0.000{col 55}{space 4} .7071787{col 68}{space 3} 2.305258
{txt}{space 9}366  {c |}{col 15}{res}{space 2}-.5362375{col 27}{space 2} .1069114{col 38}{space 1}   -5.02{col 47}{space 3}0.000{col 55}{space 4}-.7457799{col 68}{space 3} -.326695
{txt}{space 9}367  {c |}{col 15}{res}{space 2}-.2017043{col 27}{space 2} .1233275{col 38}{space 1}   -1.64{col 47}{space 3}0.102{col 55}{space 4}-.4434218{col 68}{space 3} .0400132
{txt}{space 9}368  {c |}{col 15}{res}{space 2}-.2656158{col 27}{space 2} .1570581{col 38}{space 1}   -1.69{col 47}{space 3}0.091{col 55}{space 4} -.573444{col 68}{space 3} .0422124
{txt}{space 9}375  {c |}{col 15}{res}{space 2} .9740717{col 27}{space 2} .1325914{col 38}{space 1}    7.35{col 47}{space 3}0.000{col 55}{space 4} .7141973{col 68}{space 3} 1.233946
{txt}{space 9}380  {c |}{col 15}{res}{space 2} .6376016{col 27}{space 2} .2862357{col 38}{space 1}    2.23{col 47}{space 3}0.026{col 55}{space 4} .0765899{col 68}{space 3} 1.198613
{txt}{space 9}390  {c |}{col 15}{res}{space 2}  .356094{col 27}{space 2} .1287522{col 38}{space 1}    2.77{col 47}{space 3}0.006{col 55}{space 4} .1037444{col 68}{space 3} .6084436
{txt}{space 13} {c |}
{space 8}party {c |}
{space 9}EPP  {c |}{col 15}{res}{space 2}   2.2167{col 27}{space 2} .9658573{col 38}{space 1}    2.30{col 47}{space 3}0.022{col 55}{space 4} .3236541{col 68}{space 3} 4.109745
{txt}{space 9}S&D  {c |}{col 15}{res}{space 2} 2.183555{col 27}{space 2} .9848155{col 38}{space 1}    2.22{col 47}{space 3}0.027{col 55}{space 4} .2533524{col 68}{space 3} 4.113758
{txt}{space 2}Greens/EFA  {c |}{col 15}{res}{space 2}-.7913948{col 27}{space 2} .9596088{col 38}{space 1}   -0.82{col 47}{space 3}0.410{col 55}{space 4}-2.672193{col 68}{space 3} 1.089404
{txt}{space 3}ALDE/ADLE  {c |}{col 15}{res}{space 2} 2.337211{col 27}{space 2} 1.014387{col 38}{space 1}    2.30{col 47}{space 3}0.021{col 55}{space 4} .3490499{col 68}{space 3} 4.325373
{txt}{space 9}ECR  {c |}{col 15}{res}{space 2}  4.11295{col 27}{space 2} 1.018555{col 38}{space 1}    4.04{col 47}{space 3}0.000{col 55}{space 4} 2.116619{col 68}{space 3}  6.10928
{txt}{space 8}EFDD  {c |}{col 15}{res}{space 2} .2361025{col 27}{space 2} .9574468{col 38}{space 1}    0.25{col 47}{space 3}0.805{col 55}{space 4}-1.640459{col 68}{space 3} 2.112664
{txt}{space 5}GUE-NGL  {c |}{col 15}{res}{space 2}-.9404278{col 27}{space 2} 1.233859{col 38}{space 1}   -0.76{col 47}{space 3}0.446{col 55}{space 4}-3.358748{col 68}{space 3} 1.477892
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2}-3.498392{col 27}{space 2} .9777027{col 38}{space 1}   -3.58{col 47}{space 3}0.000{col 55}{space 4}-5.414654{col 68}{space 3} -1.58213
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA29, title((A29))
{txt}
{com}. //Model A30
. logit prosanctions migrantshare2 population unemployment gdpgrowth distance col libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res: -632.7347}  
Iteration 2:{space 3}log pseudolikelihood = {res: -622.6432}  
Iteration 3:{space 3}log pseudolikelihood = {res:-622.14489}  
Iteration 4:{space 3}log pseudolikelihood = {res:-622.14052}  
Iteration 5:{space 3}log pseudolikelihood = {res:-622.14052}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(14)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-622.14052{txt}{col 49}Pseudo R2{col 67}= {res}    0.4693

{txt}{ralign 79:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1} prosanctions{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
migrantshare2 {c |}{col 15}{res}{space 2}-.7212582{col 27}{space 2} .1691792{col 38}{space 1}   -4.26{col 47}{space 3}0.000{col 55}{space 4}-1.052843{col 68}{space 3}-.3896732
{txt}{space 3}population {c |}{col 15}{res}{space 2} .0791152{col 27}{space 2} .1521132{col 38}{space 1}    0.52{col 47}{space 3}0.603{col 55}{space 4}-.2190213{col 68}{space 3} .3772517
{txt}{space 1}unemployment {c |}{col 15}{res}{space 2}-.1009754{col 27}{space 2} .0505734{col 38}{space 1}   -2.00{col 47}{space 3}0.046{col 55}{space 4}-.2000975{col 68}{space 3}-.0018533
{txt}{space 4}gdpgrowth {c |}{col 15}{res}{space 2} .0682922{col 27}{space 2}  .093994{col 38}{space 1}    0.73{col 47}{space 3}0.467{col 55}{space 4}-.1159327{col 68}{space 3}  .252517
{txt}{space 5}distance {c |}{col 15}{res}{space 2}-.0002672{col 27}{space 2} .0002252{col 38}{space 1}   -1.19{col 47}{space 3}0.235{col 55}{space 4}-.0007085{col 68}{space 3} .0001742
{txt}{space 10}col {c |}{col 15}{res}{space 2} .6248872{col 27}{space 2} .2043579{col 38}{space 1}    3.06{col 47}{space 3}0.002{col 55}{space 4} .2243531{col 68}{space 3} 1.025421
{txt}{space 4}libyabill {c |}{col 15}{res}{space 2} 3.557839{col 27}{space 2} .3809598{col 38}{space 1}    9.34{col 47}{space 3}0.000{col 55}{space 4} 2.811171{col 68}{space 3} 4.304506
{txt}{space 5}iranbill {c |}{col 15}{res}{space 2} 4.669652{col 27}{space 2} .4817285{col 38}{space 1}    9.69{col 47}{space 3}0.000{col 55}{space 4} 3.725481{col 68}{space 3} 5.613822
{txt}{space 13} {c |}
{space 8}ccode {c |}
{space 9}205  {c |}{col 15}{res}{space 2} 4.120085{col 27}{space 2} 9.104117{col 38}{space 1}    0.45{col 47}{space 3}0.651{col 55}{space 4}-13.72366{col 68}{space 3} 21.96383
{txt}{space 9}210  {c |}{col 15}{res}{space 2} 4.714896{col 27}{space 2} 7.114392{col 38}{space 1}    0.66{col 47}{space 3}0.508{col 55}{space 4}-9.229056{col 68}{space 3} 18.65885
{txt}{space 9}211  {c |}{col 15}{res}{space 2} 3.651787{col 27}{space 2} 7.998029{col 38}{space 1}    0.46{col 47}{space 3}0.648{col 55}{space 4}-12.02406{col 68}{space 3} 19.32764
{txt}{space 9}212  {c |}{col 15}{res}{space 2} 4.247311{col 27}{space 2} 9.687804{col 38}{space 1}    0.44{col 47}{space 3}0.661{col 55}{space 4}-14.74044{col 68}{space 3} 23.23506
{txt}{space 9}220  {c |}{col 15}{res}{space 2}-1.001028{col 27}{space 2} .4022443{col 38}{space 1}   -2.49{col 47}{space 3}0.013{col 55}{space 4}-1.789413{col 68}{space 3}-.2126437
{txt}{space 9}230  {c |}{col 15}{res}{space 2} 2.519837{col 27}{space 2} 2.990461{col 38}{space 1}    0.84{col 47}{space 3}0.399{col 55}{space 4}-3.341359{col 68}{space 3} 8.381033
{txt}{space 9}235  {c |}{col 15}{res}{space 2} 5.158559{col 27}{space 2} 8.106921{col 38}{space 1}    0.64{col 47}{space 3}0.525{col 55}{space 4}-10.73071{col 68}{space 3} 21.04783
{txt}{space 9}255  {c |}{col 15}{res}{space 2} -1.67637{col 27}{space 2} 2.686013{col 38}{space 1}   -0.62{col 47}{space 3}0.533{col 55}{space 4}-6.940859{col 68}{space 3} 3.588118
{txt}{space 9}290  {c |}{col 15}{res}{space 2} 1.596473{col 27}{space 2} 4.047555{col 38}{space 1}    0.39{col 47}{space 3}0.693{col 55}{space 4}-6.336588{col 68}{space 3} 9.529534
{txt}{space 9}305  {c |}{col 15}{res}{space 2} 3.765365{col 27}{space 2} 8.407863{col 38}{space 1}    0.45{col 47}{space 3}0.654{col 55}{space 4}-12.71374{col 68}{space 3} 20.24447
{txt}{space 9}310  {c |}{col 15}{res}{space 2} 3.811819{col 27}{space 2}  8.33936{col 38}{space 1}    0.46{col 47}{space 3}0.648{col 55}{space 4}-12.53303{col 68}{space 3} 20.15666
{txt}{space 9}316  {c |}{col 15}{res}{space 2} 4.166059{col 27}{space 2} 7.987491{col 38}{space 1}    0.52{col 47}{space 3}0.602{col 55}{space 4}-11.48914{col 68}{space 3} 19.82125
{txt}{space 9}317  {c |}{col 15}{res}{space 2} 4.350195{col 27}{space 2} 9.143184{col 38}{space 1}    0.48{col 47}{space 3}0.634{col 55}{space 4}-13.57012{col 68}{space 3} 22.27051
{txt}{space 9}325  {c |}{col 15}{res}{space 2}-.3422973{col 27}{space 2} .7347332{col 38}{space 1}   -0.47{col 47}{space 3}0.641{col 55}{space 4}-1.782348{col 68}{space 3} 1.097753
{txt}{space 9}338  {c |}{col 15}{res}{space 2} 5.620242{col 27}{space 2} 9.909815{col 38}{space 1}    0.57{col 47}{space 3}0.571{col 55}{space 4}-13.80264{col 68}{space 3} 25.04312
{txt}{space 9}344  {c |}{col 15}{res}{space 2} 6.340708{col 27}{space 2}  9.25253{col 38}{space 1}    0.69{col 47}{space 3}0.493{col 55}{space 4}-11.79392{col 68}{space 3} 24.47533
{txt}{space 9}349  {c |}{col 15}{res}{space 2} 4.400323{col 27}{space 2} 9.423215{col 38}{space 1}    0.47{col 47}{space 3}0.641{col 55}{space 4}-14.06884{col 68}{space 3} 22.86948
{txt}{space 9}350  {c |}{col 15}{res}{space 2} 4.244046{col 27}{space 2} 8.313682{col 38}{space 1}    0.51{col 47}{space 3}0.610{col 55}{space 4}-12.05047{col 68}{space 3} 20.53856
{txt}{space 9}352  {c |}{col 15}{res}{space 2} 5.500176{col 27}{space 2} 9.545072{col 38}{space 1}    0.58{col 47}{space 3}0.564{col 55}{space 4}-13.20782{col 68}{space 3} 24.20817
{txt}{space 9}355  {c |}{col 15}{res}{space 2}  4.30542{col 27}{space 2} 8.779195{col 38}{space 1}    0.49{col 47}{space 3}0.624{col 55}{space 4}-12.90149{col 68}{space 3} 21.51233
{txt}{space 9}360  {c |}{col 15}{res}{space 2} 5.139773{col 27}{space 2} 6.905457{col 38}{space 1}    0.74{col 47}{space 3}0.457{col 55}{space 4}-8.394675{col 68}{space 3} 18.67422
{txt}{space 9}366  {c |}{col 15}{res}{space 2} 4.660926{col 27}{space 2} 9.587416{col 38}{space 1}    0.49{col 47}{space 3}0.627{col 55}{space 4}-14.13007{col 68}{space 3} 23.45192
{txt}{space 9}367  {c |}{col 15}{res}{space 2} 5.010795{col 27}{space 2} 9.504191{col 38}{space 1}    0.53{col 47}{space 3}0.598{col 55}{space 4}-13.61708{col 68}{space 3} 23.63867
{txt}{space 9}368  {c |}{col 15}{res}{space 2} 4.959382{col 27}{space 2}  9.48813{col 38}{space 1}    0.52{col 47}{space 3}0.601{col 55}{space 4}-13.63701{col 68}{space 3} 23.55577
{txt}{space 9}375  {c |}{col 15}{res}{space 2} 5.718609{col 27}{space 2} 8.899601{col 38}{space 1}    0.64{col 47}{space 3}0.521{col 55}{space 4}-11.72429{col 68}{space 3} 23.16151
{txt}{space 9}380  {c |}{col 15}{res}{space 2} 5.159264{col 27}{space 2} 8.497112{col 38}{space 1}    0.61{col 47}{space 3}0.544{col 55}{space 4}-11.49477{col 68}{space 3}  21.8133
{txt}{space 9}390  {c |}{col 15}{res}{space 2} 5.013747{col 27}{space 2} 8.841699{col 38}{space 1}    0.57{col 47}{space 3}0.571{col 55}{space 4}-12.31567{col 68}{space 3} 22.34316
{txt}{space 13} {c |}
{space 8}party {c |}
{space 9}EPP  {c |}{col 15}{res}{space 2} 2.170398{col 27}{space 2} .9488418{col 38}{space 1}    2.29{col 47}{space 3}0.022{col 55}{space 4} .3107026{col 68}{space 3} 4.030094
{txt}{space 9}S&D  {c |}{col 15}{res}{space 2} 2.132925{col 27}{space 2}  .969714{col 38}{space 1}    2.20{col 47}{space 3}0.028{col 55}{space 4} .2323203{col 68}{space 3}  4.03353
{txt}{space 2}Greens/EFA  {c |}{col 15}{res}{space 2}-.8515824{col 27}{space 2} .9546367{col 38}{space 1}   -0.89{col 47}{space 3}0.372{col 55}{space 4}-2.722636{col 68}{space 3} 1.019471
{txt}{space 3}ALDE/ADLE  {c |}{col 15}{res}{space 2} 2.320273{col 27}{space 2} 1.003978{col 38}{space 1}    2.31{col 47}{space 3}0.021{col 55}{space 4} .3525126{col 68}{space 3} 4.288034
{txt}{space 9}ECR  {c |}{col 15}{res}{space 2} 4.039306{col 27}{space 2} .9896136{col 38}{space 1}    4.08{col 47}{space 3}0.000{col 55}{space 4} 2.099699{col 68}{space 3} 5.978913
{txt}{space 8}EFDD  {c |}{col 15}{res}{space 2} .0906024{col 27}{space 2} .9079464{col 38}{space 1}    0.10{col 47}{space 3}0.921{col 55}{space 4} -1.68894{col 68}{space 3} 1.870145
{txt}{space 5}GUE-NGL  {c |}{col 15}{res}{space 2}-.9695787{col 27}{space 2} 1.218473{col 38}{space 1}   -0.80{col 47}{space 3}0.426{col 55}{space 4}-3.357742{col 68}{space 3} 1.418585
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2} -7.05197{col 27}{space 2} 9.810348{col 38}{space 1}   -0.72{col 47}{space 3}0.472{col 55}{space 4} -26.2799{col 68}{space 3} 12.17596
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA30, title((A30))
{txt}
{com}. //Model A31
. logit prosanctions migrantshare2 population unemployment gdpgrowth distance col immigrantpolicy libyabill iranbill i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-648.52488}  
Iteration 2:{space 3}log pseudolikelihood = {res:-641.95835}  
Iteration 3:{space 3}log pseudolikelihood = {res:-641.61906}  
Iteration 4:{space 3}log pseudolikelihood = {res: -641.6157}  
Iteration 5:{space 3}log pseudolikelihood = {res:-641.61569}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}Wald chi2({res}16{txt}){col 67}= {res}    924.11
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-641.61569{txt}{col 49}Pseudo R2{col 67}= {res}    0.4527

{txt}{ralign 81:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}   prosanctions{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}migrantshare2 {c |}{col 17}{res}{space 2}-.3449104{col 29}{space 2} .0864253{col 40}{space 1}   -3.99{col 49}{space 3}0.000{col 57}{space 4}-.5143009{col 70}{space 3}  -.17552
{txt}{space 5}population {c |}{col 17}{res}{space 2}-.0068951{col 29}{space 2} .0035161{col 40}{space 1}   -1.96{col 49}{space 3}0.050{col 57}{space 4}-.0137866{col 70}{space 3}-3.70e-06
{txt}{space 3}unemployment {c |}{col 17}{res}{space 2}-.0489458{col 29}{space 2} .0239137{col 40}{space 1}   -2.05{col 49}{space 3}0.041{col 57}{space 4}-.0958159{col 70}{space 3}-.0020758
{txt}{space 6}gdpgrowth {c |}{col 17}{res}{space 2} .0272452{col 29}{space 2} .0541586{col 40}{space 1}    0.50{col 49}{space 3}0.615{col 57}{space 4}-.0789037{col 70}{space 3} .1333942
{txt}{space 7}distance {c |}{col 17}{res}{space 2} .0001286{col 29}{space 2} .0001235{col 40}{space 1}    1.04{col 49}{space 3}0.298{col 57}{space 4}-.0001134{col 70}{space 3} .0003706
{txt}{space 12}col {c |}{col 17}{res}{space 2}-.0185575{col 29}{space 2} .2015813{col 40}{space 1}   -0.09{col 49}{space 3}0.927{col 57}{space 4}-.4136495{col 70}{space 3} .3765346
{txt}immigrantpolicy {c |}{col 17}{res}{space 2}-.4014209{col 29}{space 2} .4434333{col 40}{space 1}   -0.91{col 49}{space 3}0.365{col 57}{space 4}-1.270534{col 70}{space 3} .4676924
{txt}{space 6}libyabill {c |}{col 17}{res}{space 2} 3.843469{col 29}{space 2} .3315737{col 40}{space 1}   11.59{col 49}{space 3}0.000{col 57}{space 4} 3.193597{col 70}{space 3} 4.493342
{txt}{space 7}iranbill {c |}{col 17}{res}{space 2} 3.865782{col 29}{space 2} .3574711{col 40}{space 1}   10.81{col 49}{space 3}0.000{col 57}{space 4} 3.165151{col 70}{space 3} 4.566412
{txt}{space 15} {c |}
{space 10}party {c |}
{space 11}EPP  {c |}{col 17}{res}{space 2} 2.343274{col 29}{space 2} .9341219{col 40}{space 1}    2.51{col 49}{space 3}0.012{col 57}{space 4} .5124286{col 70}{space 3} 4.174119
{txt}{space 11}S&D  {c |}{col 17}{res}{space 2} 2.367741{col 29}{space 2} .9448356{col 40}{space 1}    2.51{col 49}{space 3}0.012{col 57}{space 4} .5158972{col 70}{space 3} 4.219585
{txt}{space 4}Greens/EFA  {c |}{col 17}{res}{space 2}-.6446111{col 29}{space 2} .9151953{col 40}{space 1}   -0.70{col 49}{space 3}0.481{col 57}{space 4}-2.438361{col 70}{space 3} 1.149139
{txt}{space 5}ALDE/ADLE  {c |}{col 17}{res}{space 2} 2.523285{col 29}{space 2}  .956531{col 40}{space 1}    2.64{col 49}{space 3}0.008{col 57}{space 4} .6485183{col 70}{space 3} 4.398051
{txt}{space 11}ECR  {c |}{col 17}{res}{space 2}  4.25731{col 29}{space 2} 1.032752{col 40}{space 1}    4.12{col 49}{space 3}0.000{col 57}{space 4} 2.233154{col 70}{space 3} 6.281466
{txt}{space 10}EFDD  {c |}{col 17}{res}{space 2} .3351425{col 29}{space 2} .9288074{col 40}{space 1}    0.36{col 49}{space 3}0.718{col 57}{space 4}-1.485287{col 70}{space 3} 2.155572
{txt}{space 7}GUE-NGL  {c |}{col 17}{res}{space 2}-.6293994{col 29}{space 2} 1.124217{col 40}{space 1}   -0.56{col 49}{space 3}0.576{col 57}{space 4}-2.832824{col 70}{space 3} 1.574026
{txt}{space 15} {c |}
{space 10}_cons {c |}{col 17}{res}{space 2}-2.564968{col 29}{space 2}  1.61582{col 40}{space 1}   -1.59{col 49}{space 3}0.112{col 57}{space 4}-5.731917{col 70}{space 3} .6019814
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA31, title((A31))
{txt}
{com}. //Model A32
. logit prosanctions migrantshare2 population unemployment gdpgrowth distance col RWPvoteshare libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-629.92649}  
Iteration 2:{space 3}log pseudolikelihood = {res:-619.54876}  
Iteration 3:{space 3}log pseudolikelihood = {res:-619.02415}  
Iteration 4:{space 3}log pseudolikelihood = {res:-619.01763}  
Iteration 5:{space 3}log pseudolikelihood = {res:-619.01762}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(15)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-619.01762{txt}{col 49}Pseudo R2{col 67}= {res}    0.4720

{txt}{ralign 79:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1} prosanctions{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
migrantshare2 {c |}{col 15}{res}{space 2}-.7365359{col 27}{space 2} .1560415{col 38}{space 1}   -4.72{col 47}{space 3}0.000{col 55}{space 4}-1.042372{col 68}{space 3}-.4307002
{txt}{space 3}population {c |}{col 15}{res}{space 2} .0399373{col 27}{space 2} .1214614{col 38}{space 1}    0.33{col 47}{space 3}0.742{col 55}{space 4}-.1981227{col 68}{space 3} .2779972
{txt}{space 1}unemployment {c |}{col 15}{res}{space 2}-.1019293{col 27}{space 2} .0391857{col 38}{space 1}   -2.60{col 47}{space 3}0.009{col 55}{space 4}-.1787319{col 68}{space 3}-.0251267
{txt}{space 4}gdpgrowth {c |}{col 15}{res}{space 2} .0567024{col 27}{space 2} .0752833{col 38}{space 1}    0.75{col 47}{space 3}0.451{col 55}{space 4}-.0908502{col 68}{space 3} .2042549
{txt}{space 5}distance {c |}{col 15}{res}{space 2}-.0002366{col 27}{space 2} .0002098{col 38}{space 1}   -1.13{col 47}{space 3}0.259{col 55}{space 4}-.0006477{col 68}{space 3} .0001745
{txt}{space 10}col {c |}{col 15}{res}{space 2} .1734964{col 27}{space 2} .2775713{col 38}{space 1}    0.63{col 47}{space 3}0.532{col 55}{space 4}-.3705334{col 68}{space 3} .7175263
{txt}{space 1}RWPvoteshare {c |}{col 15}{res}{space 2}-.0918971{col 27}{space 2} .0459506{col 38}{space 1}   -2.00{col 47}{space 3}0.046{col 55}{space 4}-.1819586{col 68}{space 3}-.0018357
{txt}{space 4}libyabill {c |}{col 15}{res}{space 2} 3.745107{col 27}{space 2} .4272697{col 38}{space 1}    8.77{col 47}{space 3}0.000{col 55}{space 4} 2.907674{col 68}{space 3} 4.582541
{txt}{space 5}iranbill {c |}{col 15}{res}{space 2} 4.702199{col 27}{space 2} .4658641{col 38}{space 1}   10.09{col 47}{space 3}0.000{col 55}{space 4} 3.789122{col 68}{space 3} 5.615276
{txt}{space 13} {c |}
{space 8}ccode {c |}
{space 9}205  {c |}{col 15}{res}{space 2} 1.782287{col 27}{space 2} 7.224582{col 38}{space 1}    0.25{col 47}{space 3}0.805{col 55}{space 4}-12.37763{col 68}{space 3} 15.94221
{txt}{space 9}210  {c |}{col 15}{res}{space 2} 4.004003{col 27}{space 2} 5.777477{col 38}{space 1}    0.69{col 47}{space 3}0.488{col 55}{space 4}-7.319644{col 68}{space 3} 15.32765
{txt}{space 9}211  {c |}{col 15}{res}{space 2} 2.131911{col 27}{space 2} 6.372806{col 38}{space 1}    0.33{col 47}{space 3}0.738{col 55}{space 4}-10.35856{col 68}{space 3} 14.62238
{txt}{space 9}212  {c |}{col 15}{res}{space 2}  1.81714{col 27}{space 2} 7.724609{col 38}{space 1}    0.24{col 47}{space 3}0.814{col 55}{space 4}-13.32282{col 68}{space 3}  16.9571
{txt}{space 9}220  {c |}{col 15}{res}{space 2}  .244716{col 27}{space 2} .7196859{col 38}{space 1}    0.34{col 47}{space 3}0.734{col 55}{space 4}-1.165842{col 68}{space 3} 1.655274
{txt}{space 9}230  {c |}{col 15}{res}{space 2} 1.836679{col 27}{space 2} 2.308351{col 38}{space 1}    0.80{col 47}{space 3}0.426{col 55}{space 4}-2.687605{col 68}{space 3} 6.360962
{txt}{space 9}235  {c |}{col 15}{res}{space 2} 3.065812{col 27}{space 2} 6.425781{col 38}{space 1}    0.48{col 47}{space 3}0.633{col 55}{space 4}-9.528488{col 68}{space 3} 15.66011
{txt}{space 9}255  {c |}{col 15}{res}{space 2}-.6989887{col 27}{space 2} 2.166145{col 38}{space 1}   -0.32{col 47}{space 3}0.747{col 55}{space 4}-4.944555{col 68}{space 3} 3.546578
{txt}{space 9}290  {c |}{col 15}{res}{space 2} .6449573{col 27}{space 2} 3.229121{col 38}{space 1}    0.20{col 47}{space 3}0.842{col 55}{space 4}-5.684004{col 68}{space 3} 6.973919
{txt}{space 9}305  {c |}{col 15}{res}{space 2} 3.901156{col 27}{space 2} 6.915639{col 38}{space 1}    0.56{col 47}{space 3}0.573{col 55}{space 4}-9.653249{col 68}{space 3} 17.45556
{txt}{space 9}310  {c |}{col 15}{res}{space 2} 3.424032{col 27}{space 2}  6.73182{col 38}{space 1}    0.51{col 47}{space 3}0.611{col 55}{space 4}-9.770093{col 68}{space 3} 16.61816
{txt}{space 9}316  {c |}{col 15}{res}{space 2}  2.76766{col 27}{space 2} 6.356343{col 38}{space 1}    0.44{col 47}{space 3}0.663{col 55}{space 4}-9.690543{col 68}{space 3} 15.22586
{txt}{space 9}317  {c |}{col 15}{res}{space 2} 2.723567{col 27}{space 2} 7.238441{col 38}{space 1}    0.38{col 47}{space 3}0.707{col 55}{space 4}-11.46352{col 68}{space 3} 16.91065
{txt}{space 9}325  {c |}{col 15}{res}{space 2} .2119825{col 27}{space 2} .7273075{col 38}{space 1}    0.29{col 47}{space 3}0.771{col 55}{space 4}-1.213514{col 68}{space 3} 1.637479
{txt}{space 9}338  {c |}{col 15}{res}{space 2} 3.245031{col 27}{space 2} 7.868715{col 38}{space 1}    0.41{col 47}{space 3}0.680{col 55}{space 4}-12.17737{col 68}{space 3} 18.66743
{txt}{space 9}344  {c |}{col 15}{res}{space 2} 4.493762{col 27}{space 2} 7.318095{col 38}{space 1}    0.61{col 47}{space 3}0.539{col 55}{space 4} -9.84944{col 68}{space 3} 18.83696
{txt}{space 9}349  {c |}{col 15}{res}{space 2} 2.283904{col 27}{space 2} 7.503687{col 38}{space 1}    0.30{col 47}{space 3}0.761{col 55}{space 4}-12.42305{col 68}{space 3} 16.99086
{txt}{space 9}350  {c |}{col 15}{res}{space 2} 3.233531{col 27}{space 2} 6.581328{col 38}{space 1}    0.49{col 47}{space 3}0.623{col 55}{space 4}-9.665634{col 68}{space 3}  16.1327
{txt}{space 9}352  {c |}{col 15}{res}{space 2} 3.081316{col 27}{space 2}  7.55713{col 38}{space 1}    0.41{col 47}{space 3}0.683{col 55}{space 4}-11.73039{col 68}{space 3} 17.89302
{txt}{space 9}355  {c |}{col 15}{res}{space 2} 3.145916{col 27}{space 2} 6.989799{col 38}{space 1}    0.45{col 47}{space 3}0.653{col 55}{space 4}-10.55384{col 68}{space 3} 16.84567
{txt}{space 9}360  {c |}{col 15}{res}{space 2} 3.805755{col 27}{space 2} 5.516828{col 38}{space 1}    0.69{col 47}{space 3}0.490{col 55}{space 4} -7.00703{col 68}{space 3} 14.61854
{txt}{space 9}366  {c |}{col 15}{res}{space 2} 2.202626{col 27}{space 2} 7.618682{col 38}{space 1}    0.29{col 47}{space 3}0.772{col 55}{space 4}-12.72972{col 68}{space 3} 17.13497
{txt}{space 9}367  {c |}{col 15}{res}{space 2} 3.896563{col 27}{space 2} 7.594075{col 38}{space 1}    0.51{col 47}{space 3}0.608{col 55}{space 4}-10.98755{col 68}{space 3} 18.78068
{txt}{space 9}368  {c |}{col 15}{res}{space 2} 2.606723{col 27}{space 2} 7.551679{col 38}{space 1}    0.35{col 47}{space 3}0.730{col 55}{space 4} -12.1943{col 68}{space 3} 17.40774
{txt}{space 9}375  {c |}{col 15}{res}{space 2} 4.384154{col 27}{space 2} 7.117362{col 38}{space 1}    0.62{col 47}{space 3}0.538{col 55}{space 4}-9.565619{col 68}{space 3} 18.33393
{txt}{space 9}380  {c |}{col 15}{res}{space 2} 3.787685{col 27}{space 2} 6.733666{col 38}{space 1}    0.56{col 47}{space 3}0.574{col 55}{space 4}-9.410058{col 68}{space 3} 16.98543
{txt}{space 9}390  {c |}{col 15}{res}{space 2} 3.946854{col 27}{space 2} 7.055373{col 38}{space 1}    0.56{col 47}{space 3}0.576{col 55}{space 4}-9.881422{col 68}{space 3} 17.77513
{txt}{space 13} {c |}
{space 8}party {c |}
{space 9}EPP  {c |}{col 15}{res}{space 2} 2.165678{col 27}{space 2} .9652256{col 38}{space 1}    2.24{col 47}{space 3}0.025{col 55}{space 4} .2738709{col 68}{space 3} 4.057486
{txt}{space 9}S&D  {c |}{col 15}{res}{space 2} 2.143215{col 27}{space 2} .9888981{col 38}{space 1}    2.17{col 47}{space 3}0.030{col 55}{space 4} .2050108{col 68}{space 3}  4.08142
{txt}{space 2}Greens/EFA  {c |}{col 15}{res}{space 2}-.7891485{col 27}{space 2} .9822078{col 38}{space 1}   -0.80{col 47}{space 3}0.422{col 55}{space 4} -2.71424{col 68}{space 3} 1.135943
{txt}{space 3}ALDE/ADLE  {c |}{col 15}{res}{space 2} 2.323957{col 27}{space 2} 1.015974{col 38}{space 1}    2.29{col 47}{space 3}0.022{col 55}{space 4} .3326842{col 68}{space 3}  4.31523
{txt}{space 9}ECR  {c |}{col 15}{res}{space 2} 4.046938{col 27}{space 2} .9964812{col 38}{space 1}    4.06{col 47}{space 3}0.000{col 55}{space 4}  2.09387{col 68}{space 3} 6.000005
{txt}{space 8}EFDD  {c |}{col 15}{res}{space 2}   .11366{col 27}{space 2} .9374633{col 38}{space 1}    0.12{col 47}{space 3}0.903{col 55}{space 4}-1.723734{col 68}{space 3} 1.951054
{txt}{space 5}GUE-NGL  {c |}{col 15}{res}{space 2}-.9343845{col 27}{space 2} 1.255892{col 38}{space 1}   -0.74{col 47}{space 3}0.457{col 55}{space 4}-3.395888{col 68}{space 3} 1.527119
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2}-4.716034{col 27}{space 2} 8.263515{col 38}{space 1}   -0.57{col 47}{space 3}0.568{col 55}{space 4}-20.91222{col 68}{space 3} 11.48016
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA32, title((A32))
{txt}
{com}. //Model A33
. logit prosanctions migrantshare2 population unemployment gdpgrowth distance col exportsGDP importsGDP libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1162.7147}  
Iteration 1:{space 3}log pseudolikelihood = {res:-625.91758}  
Iteration 2:{space 3}log pseudolikelihood = {res: -615.7981}  
Iteration 3:{space 3}log pseudolikelihood = {res:-615.29279}  
Iteration 4:{space 3}log pseudolikelihood = {res:-615.28787}  
Iteration 5:{space 3}log pseudolikelihood = {res:-615.28786}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,699
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(16)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-615.28786{txt}{col 49}Pseudo R2{col 67}= {res}    0.4708

{txt}{ralign 79:(Std. Err. adjusted for {res:27} clusters in ccode)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1} prosanctions{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
migrantshare2 {c |}{col 15}{res}{space 2}-.7383095{col 27}{space 2} .1951204{col 38}{space 1}   -3.78{col 47}{space 3}0.000{col 55}{space 4}-1.120739{col 68}{space 3}-.3558806
{txt}{space 3}population {c |}{col 15}{res}{space 2} .1401633{col 27}{space 2} .1399648{col 38}{space 1}    1.00{col 47}{space 3}0.317{col 55}{space 4}-.1341627{col 68}{space 3} .4144893
{txt}{space 1}unemployment {c |}{col 15}{res}{space 2}-.0690286{col 27}{space 2} .0490386{col 38}{space 1}   -1.41{col 47}{space 3}0.159{col 55}{space 4}-.1651424{col 68}{space 3} .0270852
{txt}{space 4}gdpgrowth {c |}{col 15}{res}{space 2} .0965294{col 27}{space 2}  .092171{col 38}{space 1}    1.05{col 47}{space 3}0.295{col 55}{space 4}-.0841224{col 68}{space 3} .2771812
{txt}{space 5}distance {c |}{col 15}{res}{space 2}-.0003072{col 27}{space 2} .0002609{col 38}{space 1}   -1.18{col 47}{space 3}0.239{col 55}{space 4}-.0008185{col 68}{space 3} .0002041
{txt}{space 10}col {c |}{col 15}{res}{space 2} .6796415{col 27}{space 2} .1983046{col 38}{space 1}    3.43{col 47}{space 3}0.001{col 55}{space 4} .2909715{col 68}{space 3} 1.068311
{txt}{space 3}exportsGDP {c |}{col 15}{res}{space 2} 266.6217{col 27}{space 2} 262.5686{col 38}{space 1}    1.02{col 47}{space 3}0.310{col 55}{space 4}-248.0034{col 68}{space 3} 781.2468
{txt}{space 3}importsGDP {c |}{col 15}{res}{space 2}-871.4538{col 27}{space 2}  451.181{col 38}{space 1}   -1.93{col 47}{space 3}0.053{col 55}{space 4}-1755.752{col 68}{space 3} 12.84461
{txt}{space 4}libyabill {c |}{col 15}{res}{space 2} 3.689391{col 27}{space 2}  .351048{col 38}{space 1}   10.51{col 47}{space 3}0.000{col 55}{space 4} 3.001349{col 68}{space 3} 4.377432
{txt}{space 5}iranbill {c |}{col 15}{res}{space 2} 4.557666{col 27}{space 2} .5367623{col 38}{space 1}    8.49{col 47}{space 3}0.000{col 55}{space 4} 3.505631{col 68}{space 3} 5.609701
{txt}{space 13} {c |}
{space 8}ccode {c |}
{space 9}205  {c |}{col 15}{res}{space 2}  7.97084{col 27}{space 2} 8.430439{col 38}{space 1}    0.95{col 47}{space 3}0.344{col 55}{space 4}-8.552517{col 68}{space 3}  24.4942
{txt}{space 9}210  {c |}{col 15}{res}{space 2} 7.935246{col 27}{space 2}  6.65987{col 38}{space 1}    1.19{col 47}{space 3}0.233{col 55}{space 4} -5.11786{col 68}{space 3} 20.98835
{txt}{space 9}211  {c |}{col 15}{res}{space 2} 6.764789{col 27}{space 2} 7.343155{col 38}{space 1}    0.92{col 47}{space 3}0.357{col 55}{space 4} -7.62753{col 68}{space 3} 21.15711
{txt}{space 9}220  {c |}{col 15}{res}{space 2}-1.075034{col 27}{space 2} .3677797{col 38}{space 1}   -2.92{col 47}{space 3}0.003{col 55}{space 4}-1.795869{col 68}{space 3}-.3541994
{txt}{space 9}230  {c |}{col 15}{res}{space 2} 3.195696{col 27}{space 2} 2.753017{col 38}{space 1}    1.16{col 47}{space 3}0.246{col 55}{space 4}-2.200117{col 68}{space 3} 8.591509
{txt}{space 9}235  {c |}{col 15}{res}{space 2} 8.240007{col 27}{space 2} 7.465593{col 38}{space 1}    1.10{col 47}{space 3}0.270{col 55}{space 4}-6.392286{col 68}{space 3}  22.8723
{txt}{space 9}255  {c |}{col 15}{res}{space 2}-2.602223{col 27}{space 2} 2.459913{col 38}{space 1}   -1.06{col 47}{space 3}0.290{col 55}{space 4}-7.423563{col 68}{space 3} 2.219118
{txt}{space 9}290  {c |}{col 15}{res}{space 2} 2.974632{col 27}{space 2} 3.688045{col 38}{space 1}    0.81{col 47}{space 3}0.420{col 55}{space 4}-4.253805{col 68}{space 3} 10.20307
{txt}{space 9}305  {c |}{col 15}{res}{space 2}  7.60139{col 27}{space 2} 7.851794{col 38}{space 1}    0.97{col 47}{space 3}0.333{col 55}{space 4}-7.787844{col 68}{space 3} 22.99062
{txt}{space 9}310  {c |}{col 15}{res}{space 2} 6.874813{col 27}{space 2} 7.639851{col 38}{space 1}    0.90{col 47}{space 3}0.368{col 55}{space 4}-8.099019{col 68}{space 3} 21.84864
{txt}{space 9}316  {c |}{col 15}{res}{space 2}  7.38938{col 27}{space 2} 7.319679{col 38}{space 1}    1.01{col 47}{space 3}0.313{col 55}{space 4}-6.956928{col 68}{space 3} 21.73569
{txt}{space 9}317  {c |}{col 15}{res}{space 2} 7.596702{col 27}{space 2} 8.347241{col 38}{space 1}    0.91{col 47}{space 3}0.363{col 55}{space 4}-8.763589{col 68}{space 3} 23.95699
{txt}{space 9}325  {c |}{col 15}{res}{space 2} .0004575{col 27}{space 2} .7584161{col 38}{space 1}    0.00{col 47}{space 3}1.000{col 55}{space 4}-1.486011{col 68}{space 3} 1.486926
{txt}{space 9}338  {c |}{col 15}{res}{space 2}  8.53674{col 27}{space 2} 9.027846{col 38}{space 1}    0.95{col 47}{space 3}0.344{col 55}{space 4}-9.157514{col 68}{space 3} 26.23099
{txt}{space 9}344  {c |}{col 15}{res}{space 2} 9.974474{col 27}{space 2} 8.525574{col 38}{space 1}    1.17{col 47}{space 3}0.242{col 55}{space 4}-6.735344{col 68}{space 3} 26.68429
{txt}{space 9}349  {c |}{col 15}{res}{space 2} 7.913214{col 27}{space 2} 8.619954{col 38}{space 1}    0.92{col 47}{space 3}0.359{col 55}{space 4}-8.981586{col 68}{space 3} 24.80801
{txt}{space 9}350  {c |}{col 15}{res}{space 2} 7.600662{col 27}{space 2} 7.697392{col 38}{space 1}    0.99{col 47}{space 3}0.323{col 55}{space 4} -7.48595{col 68}{space 3} 22.68727
{txt}{space 9}352  {c |}{col 15}{res}{space 2} 9.174781{col 27}{space 2}  8.73947{col 38}{space 1}    1.05{col 47}{space 3}0.294{col 55}{space 4}-7.954267{col 68}{space 3} 26.30383
{txt}{space 9}355  {c |}{col 15}{res}{space 2} 7.624901{col 27}{space 2} 8.096837{col 38}{space 1}    0.94{col 47}{space 3}0.346{col 55}{space 4}-8.244608{col 68}{space 3} 23.49441
{txt}{space 9}360  {c |}{col 15}{res}{space 2} 7.627747{col 27}{space 2}   6.3851{col 38}{space 1}    1.19{col 47}{space 3}0.232{col 55}{space 4} -4.88682{col 68}{space 3} 20.14231
{txt}{space 9}366  {c |}{col 15}{res}{space 2} 8.276939{col 27}{space 2} 8.774973{col 38}{space 1}    0.94{col 47}{space 3}0.346{col 55}{space 4}-8.921692{col 68}{space 3} 25.47557
{txt}{space 9}367  {c |}{col 15}{res}{space 2} 8.196041{col 27}{space 2} 8.690075{col 38}{space 1}    0.94{col 47}{space 3}0.346{col 55}{space 4}-8.836193{col 68}{space 3} 25.22828
{txt}{space 9}368  {c |}{col 15}{res}{space 2} 8.159031{col 27}{space 2} 8.669368{col 38}{space 1}    0.94{col 47}{space 3}0.347{col 55}{space 4}-8.832619{col 68}{space 3} 25.15068
{txt}{space 9}375  {c |}{col 15}{res}{space 2} 9.180285{col 27}{space 2} 8.195223{col 38}{space 1}    1.12{col 47}{space 3}0.263{col 55}{space 4}-6.882056{col 68}{space 3} 25.24263
{txt}{space 9}380  {c |}{col 15}{res}{space 2} 8.419357{col 27}{space 2} 7.815395{col 38}{space 1}    1.08{col 47}{space 3}0.281{col 55}{space 4}-6.898536{col 68}{space 3} 23.73725
{txt}{space 9}390  {c |}{col 15}{res}{space 2}  8.58178{col 27}{space 2} 8.143004{col 38}{space 1}    1.05{col 47}{space 3}0.292{col 55}{space 4}-7.378214{col 68}{space 3} 24.54177
{txt}{space 13} {c |}
{space 8}party {c |}
{space 9}EPP  {c |}{col 15}{res}{space 2} 2.139453{col 27}{space 2} .9626549{col 38}{space 1}    2.22{col 47}{space 3}0.026{col 55}{space 4} .2526846{col 68}{space 3} 4.026222
{txt}{space 9}S&D  {c |}{col 15}{res}{space 2} 2.104208{col 27}{space 2} .9842506{col 38}{space 1}    2.14{col 47}{space 3}0.033{col 55}{space 4} .1751118{col 68}{space 3} 4.033303
{txt}{space 2}Greens/EFA  {c |}{col 15}{res}{space 2}-.8470192{col 27}{space 2}   .97179{col 38}{space 1}   -0.87{col 47}{space 3}0.383{col 55}{space 4}-2.751693{col 68}{space 3} 1.057654
{txt}{space 3}ALDE/ADLE  {c |}{col 15}{res}{space 2} 2.231282{col 27}{space 2} 1.010913{col 38}{space 1}    2.21{col 47}{space 3}0.027{col 55}{space 4} .2499289{col 68}{space 3} 4.212635
{txt}{space 9}ECR  {c |}{col 15}{res}{space 2} 4.048486{col 27}{space 2} .9822765{col 38}{space 1}    4.12{col 47}{space 3}0.000{col 55}{space 4} 2.123259{col 68}{space 3} 5.973713
{txt}{space 8}EFDD  {c |}{col 15}{res}{space 2} .0713197{col 27}{space 2} .9309249{col 38}{space 1}    0.08{col 47}{space 3}0.939{col 55}{space 4} -1.75326{col 68}{space 3} 1.895899
{txt}{space 5}GUE-NGL  {c |}{col 15}{res}{space 2}-.9654515{col 27}{space 2} 1.235564{col 38}{space 1}   -0.78{col 47}{space 3}0.435{col 55}{space 4}-3.387113{col 68}{space 3}  1.45621
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2}-11.03717{col 27}{space 2} 9.188496{col 38}{space 1}   -1.20{col 47}{space 3}0.230{col 55}{space 4}-29.04629{col 68}{space 3} 6.971948
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA33, title((A33))
{txt}
{com}. //Model A34
. logit prosanctions migrantshare2 population unemployment gdpgrowth distance col pincometax sstax libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1045.4561}  
Iteration 1:{space 3}log pseudolikelihood = {res:-587.99832}  
Iteration 2:{space 3}log pseudolikelihood = {res:-581.15239}  
Iteration 3:{space 3}log pseudolikelihood = {res:-580.79772}  
Iteration 4:{space 3}log pseudolikelihood = {res:-580.79502}  
Iteration 5:{space 3}log pseudolikelihood = {res:-580.79502}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,522
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(16)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-580.79502{txt}{col 49}Pseudo R2{col 67}= {res}    0.4445

{txt}{ralign 79:(Std. Err. adjusted for {res:22} clusters in ccode)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1} prosanctions{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
migrantshare2 {c |}{col 15}{res}{space 2}-.9046418{col 27}{space 2} .2103713{col 38}{space 1}   -4.30{col 47}{space 3}0.000{col 55}{space 4}-1.316962{col 68}{space 3}-.4923216
{txt}{space 3}population {c |}{col 15}{res}{space 2}-.0261809{col 27}{space 2} .1063067{col 38}{space 1}   -0.25{col 47}{space 3}0.805{col 55}{space 4}-.2345381{col 68}{space 3} .1821764
{txt}{space 1}unemployment {c |}{col 15}{res}{space 2}-.0348393{col 27}{space 2} .0514499{col 38}{space 1}   -0.68{col 47}{space 3}0.498{col 55}{space 4}-.1356793{col 68}{space 3} .0660008
{txt}{space 4}gdpgrowth {c |}{col 15}{res}{space 2}-.0261853{col 27}{space 2} .1251823{col 38}{space 1}   -0.21{col 47}{space 3}0.834{col 55}{space 4}-.2715382{col 68}{space 3} .2191675
{txt}{space 5}distance {c |}{col 15}{res}{space 2} -.000589{col 27}{space 2} .0002021{col 38}{space 1}   -2.91{col 47}{space 3}0.004{col 55}{space 4}-.0009852{col 68}{space 3}-.0001928
{txt}{space 10}col {c |}{col 15}{res}{space 2} .3942468{col 27}{space 2} .4446711{col 38}{space 1}    0.89{col 47}{space 3}0.375{col 55}{space 4}-.4772925{col 68}{space 3} 1.265786
{txt}{space 3}pincometax {c |}{col 15}{res}{space 2} -.468926{col 27}{space 2} .2479437{col 38}{space 1}   -1.89{col 47}{space 3}0.059{col 55}{space 4}-.9548867{col 68}{space 3} .0170347
{txt}{space 8}sstax {c |}{col 15}{res}{space 2} .0025737{col 27}{space 2} .5992276{col 38}{space 1}    0.00{col 47}{space 3}0.997{col 55}{space 4}-1.171891{col 68}{space 3} 1.177038
{txt}{space 4}libyabill {c |}{col 15}{res}{space 2} 3.416236{col 27}{space 2} .4429211{col 38}{space 1}    7.71{col 47}{space 3}0.000{col 55}{space 4} 2.548126{col 68}{space 3} 4.284345
{txt}{space 5}iranbill {c |}{col 15}{res}{space 2} 5.082056{col 27}{space 2} .4733508{col 38}{space 1}   10.74{col 47}{space 3}0.000{col 55}{space 4} 4.154306{col 68}{space 3} 6.009807
{txt}{space 13} {c |}
{space 8}ccode {c |}
{space 9}205  {c |}{col 15}{res}{space 2}-2.398252{col 27}{space 2} 6.335446{col 38}{space 1}   -0.38{col 47}{space 3}0.705{col 55}{space 4} -14.8155{col 68}{space 3}   10.019
{txt}{space 9}210  {c |}{col 15}{res}{space 2}-1.250014{col 27}{space 2} 7.177178{col 38}{space 1}   -0.17{col 47}{space 3}0.862{col 55}{space 4}-15.31703{col 68}{space 3}   12.817
{txt}{space 9}211  {c |}{col 15}{res}{space 2}-.5888933{col 27}{space 2} 7.961047{col 38}{space 1}   -0.07{col 47}{space 3}0.941{col 55}{space 4}-16.19226{col 68}{space 3} 15.01447
{txt}{space 9}212  {c |}{col 15}{res}{space 2}-2.592797{col 27}{space 2} 7.789452{col 38}{space 1}   -0.33{col 47}{space 3}0.739{col 55}{space 4}-17.85984{col 68}{space 3} 12.67425
{txt}{space 9}220  {c |}{col 15}{res}{space 2}-1.673874{col 27}{space 2} 7.111157{col 38}{space 1}   -0.24{col 47}{space 3}0.814{col 55}{space 4}-15.61149{col 68}{space 3} 12.26374
{txt}{space 9}230  {c |}{col 15}{res}{space 2}-1.531296{col 27}{space 2} 3.986286{col 38}{space 1}   -0.38{col 47}{space 3}0.701{col 55}{space 4}-9.344273{col 68}{space 3} 6.281681
{txt}{space 9}235  {c |}{col 15}{res}{space 2}-2.099648{col 27}{space 2} 6.101636{col 38}{space 1}   -0.34{col 47}{space 3}0.731{col 55}{space 4}-14.05864{col 68}{space 3} 9.859339
{txt}{space 9}255  {c |}{col 15}{res}{space 2} .0846898{col 27}{space 2} 4.824873{col 38}{space 1}    0.02{col 47}{space 3}0.986{col 55}{space 4}-9.371888{col 68}{space 3} 9.541268
{txt}{space 9}290  {c |}{col 15}{res}{space 2}-3.693909{col 27}{space 2} 4.657657{col 38}{space 1}   -0.79{col 47}{space 3}0.428{col 55}{space 4}-12.82275{col 68}{space 3} 5.434931
{txt}{space 9}305  {c |}{col 15}{res}{space 2}-2.002576{col 27}{space 2} 9.354318{col 38}{space 1}   -0.21{col 47}{space 3}0.830{col 55}{space 4} -20.3367{col 68}{space 3} 16.33155
{txt}{space 9}310  {c |}{col 15}{res}{space 2}-4.001029{col 27}{space 2} 7.319079{col 38}{space 1}   -0.55{col 47}{space 3}0.585{col 55}{space 4}-18.34616{col 68}{space 3}  10.3441
{txt}{space 9}316  {c |}{col 15}{res}{space 2} -4.49131{col 27}{space 2} 7.879501{col 38}{space 1}   -0.57{col 47}{space 3}0.569{col 55}{space 4}-19.93485{col 68}{space 3} 10.95223
{txt}{space 9}317  {c |}{col 15}{res}{space 2}-5.476726{col 27}{space 2} 7.934639{col 38}{space 1}   -0.69{col 47}{space 3}0.490{col 55}{space 4}-21.02833{col 68}{space 3} 10.07488
{txt}{space 9}325  {c |}{col 15}{res}{space 2}-.3611465{col 27}{space 2} 4.373058{col 38}{space 1}   -0.08{col 47}{space 3}0.934{col 55}{space 4}-8.932183{col 68}{space 3}  8.20989
{txt}{space 9}349  {c |}{col 15}{res}{space 2}-4.519158{col 27}{space 2} 8.739699{col 38}{space 1}   -0.52{col 47}{space 3}0.605{col 55}{space 4}-21.64865{col 68}{space 3} 12.61034
{txt}{space 9}350  {c |}{col 15}{res}{space 2}-5.050325{col 27}{space 2} 6.601529{col 38}{space 1}   -0.77{col 47}{space 3}0.444{col 55}{space 4}-17.98908{col 68}{space 3} 7.888435
{txt}{space 9}366  {c |}{col 15}{res}{space 2}-3.994902{col 27}{space 2} 7.874543{col 38}{space 1}   -0.51{col 47}{space 3}0.612{col 55}{space 4}-19.42872{col 68}{space 3} 11.43892
{txt}{space 9}367  {c |}{col 15}{res}{space 2}-3.657371{col 27}{space 2} 6.957209{col 38}{space 1}   -0.53{col 47}{space 3}0.599{col 55}{space 4}-17.29325{col 68}{space 3} 9.978508
{txt}{space 9}375  {c |}{col 15}{res}{space 2} .9155769{col 27}{space 2} 7.773954{col 38}{space 1}    0.12{col 47}{space 3}0.906{col 55}{space 4}-14.32109{col 68}{space 3} 16.15225
{txt}{space 9}380  {c |}{col 15}{res}{space 2} 1.142292{col 27}{space 2} 8.375358{col 38}{space 1}    0.14{col 47}{space 3}0.892{col 55}{space 4}-15.27311{col 68}{space 3} 17.55769
{txt}{space 9}390  {c |}{col 15}{res}{space 2} 6.636063{col 27}{space 2} 7.263758{col 38}{space 1}    0.91{col 47}{space 3}0.361{col 55}{space 4} -7.60064{col 68}{space 3} 20.87277
{txt}{space 13} {c |}
{space 8}party {c |}
{space 9}EPP  {c |}{col 15}{res}{space 2} 2.272101{col 27}{space 2} 1.020363{col 38}{space 1}    2.23{col 47}{space 3}0.026{col 55}{space 4} .2722265{col 68}{space 3} 4.271976
{txt}{space 9}S&D  {c |}{col 15}{res}{space 2} 2.239634{col 27}{space 2}  1.03621{col 38}{space 1}    2.16{col 47}{space 3}0.031{col 55}{space 4}    .2087{col 68}{space 3} 4.270568
{txt}{space 2}Greens/EFA  {c |}{col 15}{res}{space 2}-.6750288{col 27}{space 2} 1.034264{col 38}{space 1}   -0.65{col 47}{space 3}0.514{col 55}{space 4}-2.702149{col 68}{space 3} 1.352091
{txt}{space 3}ALDE/ADLE  {c |}{col 15}{res}{space 2} 2.468966{col 27}{space 2} 1.080418{col 38}{space 1}    2.29{col 47}{space 3}0.022{col 55}{space 4} .3513853{col 68}{space 3} 4.586546
{txt}{space 9}ECR  {c |}{col 15}{res}{space 2}  4.20786{col 27}{space 2} 1.035425{col 38}{space 1}    4.06{col 47}{space 3}0.000{col 55}{space 4} 2.178464{col 68}{space 3} 6.237256
{txt}{space 8}EFDD  {c |}{col 15}{res}{space 2} .1206931{col 27}{space 2}  .957314{col 38}{space 1}    0.13{col 47}{space 3}0.900{col 55}{space 4}-1.755608{col 68}{space 3} 1.996994
{txt}{space 5}GUE-NGL  {c |}{col 15}{res}{space 2}-.8310919{col 27}{space 2} 1.328209{col 38}{space 1}   -0.63{col 47}{space 3}0.531{col 55}{space 4}-3.434334{col 68}{space 3}  1.77215
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2} 4.660607{col 27}{space 2} 8.080213{col 38}{space 1}    0.58{col 47}{space 3}0.564{col 55}{space 4}-11.17632{col 68}{space 3} 20.49753
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA34, title((A34))
{txt}
{com}. //Model A35
. logit prosanctions migrantshare2 population unemployment gdpgrowth distance col refugees_pertotmigrants libyabill iranbill i.ccode i.party, cluster(ccode)

{txt}note: 349.ccode != 0 predicts success perfectly
      349.ccode dropped and 6 obs not used

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1106.6407}  
Iteration 1:{space 3}log pseudolikelihood = {res: -590.9675}  
Iteration 2:{space 3}log pseudolikelihood = {res:-580.95169}  
Iteration 3:{space 3}log pseudolikelihood = {res:-580.42873}  
Iteration 4:{space 3}log pseudolikelihood = {res:-580.42205}  
Iteration 5:{space 3}log pseudolikelihood = {res:-580.42205}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,615
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(15)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-580.42205{txt}{col 49}Pseudo R2{col 67}= {res}    0.4755

{txt}{ralign 89:(Std. Err. adjusted for {res:25} clusters in ccode)}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}           prosanctions{col 25}{c |}      Coef.{col 37}   Std. Err.{col 49}      z{col 57}   P>|z|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}migrantshare2 {c |}{col 25}{res}{space 2}-.7886106{col 37}{space 2} .2451184{col 48}{space 1}   -3.22{col 57}{space 3}0.001{col 65}{space 4}-1.269034{col 78}{space 3}-.3081874
{txt}{space 13}population {c |}{col 25}{res}{space 2} .0335262{col 37}{space 2} .1517039{col 48}{space 1}    0.22{col 57}{space 3}0.825{col 65}{space 4}-.2638081{col 78}{space 3} .3308604
{txt}{space 11}unemployment {c |}{col 25}{res}{space 2} -.094503{col 37}{space 2} .0487754{col 48}{space 1}   -1.94{col 57}{space 3}0.053{col 65}{space 4}-.1901011{col 78}{space 3} .0010951
{txt}{space 14}gdpgrowth {c |}{col 25}{res}{space 2} .0880901{col 37}{space 2} .1000915{col 48}{space 1}    0.88{col 57}{space 3}0.379{col 65}{space 4}-.1080857{col 78}{space 3} .2842659
{txt}{space 15}distance {c |}{col 25}{res}{space 2} -.000578{col 37}{space 2} .0002593{col 48}{space 1}   -2.23{col 57}{space 3}0.026{col 65}{space 4}-.0010862{col 78}{space 3}-.0000699
{txt}{space 20}col {c |}{col 25}{res}{space 2} .6894924{col 37}{space 2} .2209116{col 48}{space 1}    3.12{col 57}{space 3}0.002{col 65}{space 4} .2565136{col 78}{space 3} 1.122471
{txt}refugees_pertotmigrants {c |}{col 25}{res}{space 2}-.4971324{col 37}{space 2} 3.175008{col 48}{space 1}   -0.16{col 57}{space 3}0.876{col 65}{space 4}-6.720034{col 78}{space 3}  5.72577
{txt}{space 14}libyabill {c |}{col 25}{res}{space 2} 3.346121{col 37}{space 2}  .430453{col 48}{space 1}    7.77{col 57}{space 3}0.000{col 65}{space 4} 2.502449{col 78}{space 3} 4.189794
{txt}{space 15}iranbill {c |}{col 25}{res}{space 2} 5.171866{col 37}{space 2} .5323419{col 48}{space 1}    9.72{col 57}{space 3}0.000{col 65}{space 4} 4.128495{col 78}{space 3} 6.215237
{txt}{space 23} {c |}
{space 18}ccode {c |}
{space 19}205  {c |}{col 25}{res}{space 2} 1.433923{col 37}{space 2} 9.053443{col 48}{space 1}    0.16{col 57}{space 3}0.874{col 65}{space 4} -16.3105{col 78}{space 3} 19.17835
{txt}{space 19}210  {c |}{col 25}{res}{space 2} 2.533243{col 37}{space 2} 7.092188{col 48}{space 1}    0.36{col 57}{space 3}0.721{col 65}{space 4}-11.36719{col 78}{space 3} 16.43368
{txt}{space 19}211  {c |}{col 25}{res}{space 2} 1.067492{col 37}{space 2} 7.959417{col 48}{space 1}    0.13{col 57}{space 3}0.893{col 65}{space 4}-14.53268{col 78}{space 3} 16.66766
{txt}{space 19}212  {c |}{col 25}{res}{space 2} 1.010522{col 37}{space 2} 9.655667{col 48}{space 1}    0.10{col 57}{space 3}0.917{col 65}{space 4}-17.91424{col 78}{space 3} 19.93528
{txt}{space 19}220  {c |}{col 25}{res}{space 2}-1.163045{col 37}{space 2} .4272753{col 48}{space 1}   -2.72{col 57}{space 3}0.006{col 65}{space 4}-2.000489{col 78}{space 3}-.3256004
{txt}{space 19}230  {c |}{col 25}{res}{space 2} 1.526931{col 37}{space 2} 2.855051{col 48}{space 1}    0.53{col 57}{space 3}0.593{col 65}{space 4}-4.068866{col 78}{space 3} 7.122728
{txt}{space 19}235  {c |}{col 25}{res}{space 2}  2.40388{col 37}{space 2} 8.016014{col 48}{space 1}    0.30{col 57}{space 3}0.764{col 65}{space 4}-13.30722{col 78}{space 3} 18.11498
{txt}{space 19}255  {c |}{col 25}{res}{space 2}-1.059862{col 37}{space 2} 2.677248{col 48}{space 1}   -0.40{col 57}{space 3}0.692{col 65}{space 4}-6.307172{col 78}{space 3} 4.187448
{txt}{space 19}290  {c |}{col 25}{res}{space 2} .0022908{col 37}{space 2} 4.011993{col 48}{space 1}    0.00{col 57}{space 3}1.000{col 65}{space 4}-7.861071{col 78}{space 3} 7.865653
{txt}{space 19}305  {c |}{col 25}{res}{space 2} .8956454{col 37}{space 2} 8.392481{col 48}{space 1}    0.11{col 57}{space 3}0.915{col 65}{space 4}-15.55332{col 78}{space 3} 17.34461
{txt}{space 19}310  {c |}{col 25}{res}{space 2} .8493253{col 37}{space 2} 8.277071{col 48}{space 1}    0.10{col 57}{space 3}0.918{col 65}{space 4}-15.37344{col 78}{space 3} 17.07209
{txt}{space 19}316  {c |}{col 25}{res}{space 2} 1.433001{col 37}{space 2} 7.932469{col 48}{space 1}    0.18{col 57}{space 3}0.857{col 65}{space 4}-14.11435{col 78}{space 3} 16.98035
{txt}{space 19}317  {c |}{col 25}{res}{space 2} 1.140998{col 37}{space 2} 9.067007{col 48}{space 1}    0.13{col 57}{space 3}0.900{col 65}{space 4}-16.63001{col 78}{space 3} 18.91201
{txt}{space 19}325  {c |}{col 25}{res}{space 2}-.9589751{col 37}{space 2} .6766722{col 48}{space 1}   -1.42{col 57}{space 3}0.156{col 65}{space 4}-2.285228{col 78}{space 3}  .367278
{txt}{space 19}338  {c |}{col 25}{res}{space 2} 2.403638{col 37}{space 2} 9.976765{col 48}{space 1}    0.24{col 57}{space 3}0.810{col 65}{space 4}-17.15046{col 78}{space 3} 21.95774
{txt}{space 19}349  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (empty)
{space 19}350  {c |}{col 25}{res}{space 2} 1.172162{col 37}{space 2} 8.148081{col 48}{space 1}    0.14{col 57}{space 3}0.886{col 65}{space 4}-14.79778{col 78}{space 3} 17.14211
{txt}{space 19}352  {c |}{col 25}{res}{space 2} 2.044004{col 37}{space 2} 9.455632{col 48}{space 1}    0.22{col 57}{space 3}0.829{col 65}{space 4} -16.4887{col 78}{space 3}  20.5767
{txt}{space 19}355  {c |}{col 25}{res}{space 2} 1.152021{col 37}{space 2} 8.709025{col 48}{space 1}    0.13{col 57}{space 3}0.895{col 65}{space 4}-15.91736{col 78}{space 3}  18.2214
{txt}{space 19}360  {c |}{col 25}{res}{space 2} 2.837495{col 37}{space 2} 6.801439{col 48}{space 1}    0.42{col 57}{space 3}0.677{col 65}{space 4}-10.49308{col 78}{space 3} 16.16807
{txt}{space 19}367  {c |}{col 25}{res}{space 2} .4619482{col 37}{space 2} 9.436083{col 48}{space 1}    0.05{col 57}{space 3}0.961{col 65}{space 4}-18.03243{col 78}{space 3} 18.95633
{txt}{space 19}368  {c |}{col 25}{res}{space 2} .3856793{col 37}{space 2} 9.418439{col 48}{space 1}    0.04{col 57}{space 3}0.967{col 65}{space 4}-18.07412{col 78}{space 3} 18.84548
{txt}{space 19}375  {c |}{col 25}{res}{space 2} 2.964077{col 37}{space 2} 8.848406{col 48}{space 1}    0.33{col 57}{space 3}0.738{col 65}{space 4}-14.37848{col 78}{space 3} 20.30664
{txt}{space 19}380  {c |}{col 25}{res}{space 2} 2.688213{col 37}{space 2} 8.457567{col 48}{space 1}    0.32{col 57}{space 3}0.751{col 65}{space 4}-13.88831{col 78}{space 3} 19.26474
{txt}{space 19}390  {c |}{col 25}{res}{space 2} 2.355858{col 37}{space 2} 8.849996{col 48}{space 1}    0.27{col 57}{space 3}0.790{col 65}{space 4}-14.98981{col 78}{space 3} 19.70153
{txt}{space 23} {c |}
{space 18}party {c |}
{space 19}EPP  {c |}{col 25}{res}{space 2} 2.188099{col 37}{space 2} .9704113{col 48}{space 1}    2.25{col 57}{space 3}0.024{col 65}{space 4} .2861283{col 78}{space 3} 4.090071
{txt}{space 19}S&D  {c |}{col 25}{res}{space 2} 2.105472{col 37}{space 2} .9973102{col 48}{space 1}    2.11{col 57}{space 3}0.035{col 65}{space 4} .1507799{col 78}{space 3} 4.060164
{txt}{space 12}Greens/EFA  {c |}{col 25}{res}{space 2}-.9354164{col 37}{space 2} .9861536{col 48}{space 1}   -0.95{col 57}{space 3}0.343{col 65}{space 4}-2.868242{col 78}{space 3} .9974091
{txt}{space 13}ALDE/ADLE  {c |}{col 25}{res}{space 2} 2.238873{col 37}{space 2}  1.03122{col 48}{space 1}    2.17{col 57}{space 3}0.030{col 65}{space 4} .2177183{col 78}{space 3} 4.260028
{txt}{space 19}ECR  {c |}{col 25}{res}{space 2} 4.018454{col 37}{space 2}  1.00442{col 48}{space 1}    4.00{col 57}{space 3}0.000{col 65}{space 4} 2.049827{col 78}{space 3} 5.987081
{txt}{space 18}EFDD  {c |}{col 25}{res}{space 2}-.0720217{col 37}{space 2} .9129797{col 48}{space 1}   -0.08{col 57}{space 3}0.937{col 65}{space 4}-1.861429{col 78}{space 3} 1.717386
{txt}{space 15}GUE-NGL  {c |}{col 25}{res}{space 2}-1.244989{col 37}{space 2} 1.290165{col 48}{space 1}   -0.96{col 57}{space 3}0.335{col 65}{space 4}-3.773667{col 78}{space 3} 1.283688
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2}-3.077711{col 37}{space 2} 9.725558{col 48}{space 1}   -0.32{col 57}{space 3}0.752{col 65}{space 4}-22.13945{col 78}{space 3} 15.98403
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA35, title((A35))
{txt}
{com}. //Model A36
. logit prosanctions migrantshare2 population unemployment gdpgrowth distance col lnremit libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1087.5167}  
Iteration 1:{space 3}log pseudolikelihood = {res:-604.53386}  
Iteration 2:{space 3}log pseudolikelihood = {res:-594.73983}  
Iteration 3:{space 3}log pseudolikelihood = {res:-594.25415}  
Iteration 4:{space 3}log pseudolikelihood = {res:-594.24905}  
Iteration 5:{space 3}log pseudolikelihood = {res:-594.24905}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,576
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(15)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-594.24905{txt}{col 49}Pseudo R2{col 67}= {res}    0.4536

{txt}{ralign 79:(Std. Err. adjusted for {res:27} clusters in ccode)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1} prosanctions{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
migrantshare2 {c |}{col 15}{res}{space 2}-.9832136{col 27}{space 2} .1708817{col 38}{space 1}   -5.75{col 47}{space 3}0.000{col 55}{space 4}-1.318135{col 68}{space 3}-.6482917
{txt}{space 3}population {c |}{col 15}{res}{space 2} .1696712{col 27}{space 2} .1448253{col 38}{space 1}    1.17{col 47}{space 3}0.241{col 55}{space 4}-.1141812{col 68}{space 3} .4535236
{txt}{space 1}unemployment {c |}{col 15}{res}{space 2}-.0974483{col 27}{space 2} .0519552{col 38}{space 1}   -1.88{col 47}{space 3}0.061{col 55}{space 4}-.1992786{col 68}{space 3}  .004382
{txt}{space 4}gdpgrowth {c |}{col 15}{res}{space 2} .0339201{col 27}{space 2} .0975402{col 38}{space 1}    0.35{col 47}{space 3}0.728{col 55}{space 4}-.1572552{col 68}{space 3} .2250954
{txt}{space 5}distance {c |}{col 15}{res}{space 2} -.000744{col 27}{space 2} .0003069{col 38}{space 1}   -2.42{col 47}{space 3}0.015{col 55}{space 4}-.0013455{col 68}{space 3}-.0001425
{txt}{space 10}col {c |}{col 15}{res}{space 2} .8120808{col 27}{space 2} .1953208{col 38}{space 1}    4.16{col 47}{space 3}0.000{col 55}{space 4} .4292591{col 68}{space 3} 1.194903
{txt}{space 6}lnremit {c |}{col 15}{res}{space 2}-.0281466{col 27}{space 2} .1419791{col 38}{space 1}   -0.20{col 47}{space 3}0.843{col 55}{space 4}-.3064204{col 68}{space 3} .2501273
{txt}{space 4}libyabill {c |}{col 15}{res}{space 2} 2.794726{col 27}{space 2} .5525356{col 38}{space 1}    5.06{col 47}{space 3}0.000{col 55}{space 4} 1.711777{col 68}{space 3} 3.877676
{txt}{space 5}iranbill {c |}{col 15}{res}{space 2} 5.156011{col 27}{space 2} .4656928{col 38}{space 1}   11.07{col 47}{space 3}0.000{col 55}{space 4}  4.24327{col 68}{space 3} 6.068752
{txt}{space 13} {c |}
{space 8}ccode {c |}
{space 9}205  {c |}{col 15}{res}{space 2} 9.744683{col 27}{space 2} 8.530539{col 38}{space 1}    1.14{col 47}{space 3}0.253{col 55}{space 4}-6.974866{col 68}{space 3} 26.46423
{txt}{space 9}210  {c |}{col 15}{res}{space 2} 8.722587{col 27}{space 2} 6.743686{col 38}{space 1}    1.29{col 47}{space 3}0.196{col 55}{space 4}-4.494795{col 68}{space 3} 21.93997
{txt}{space 9}211  {c |}{col 15}{res}{space 2} 8.155154{col 27}{space 2} 7.607625{col 38}{space 1}    1.07{col 47}{space 3}0.284{col 55}{space 4}-6.755517{col 68}{space 3} 23.06582
{txt}{space 9}212  {c |}{col 15}{res}{space 2} 9.417839{col 27}{space 2} 9.097017{col 38}{space 1}    1.04{col 47}{space 3}0.301{col 55}{space 4}-8.411986{col 68}{space 3} 27.24766
{txt}{space 9}220  {c |}{col 15}{res}{space 2}-1.565848{col 27}{space 2} .3875891{col 38}{space 1}   -4.04{col 47}{space 3}0.000{col 55}{space 4}-2.325508{col 68}{space 3}-.8061869
{txt}{space 9}230  {c |}{col 15}{res}{space 2} 3.727088{col 27}{space 2} 2.910487{col 38}{space 1}    1.28{col 47}{space 3}0.200{col 55}{space 4}-1.977361{col 68}{space 3} 9.431537
{txt}{space 9}235  {c |}{col 15}{res}{space 2} 9.820543{col 27}{space 2} 7.582998{col 38}{space 1}    1.30{col 47}{space 3}0.195{col 55}{space 4}-5.041859{col 68}{space 3} 24.68295
{txt}{space 9}255  {c |}{col 15}{res}{space 2}-3.496583{col 27}{space 2} 2.482162{col 38}{space 1}   -1.41{col 47}{space 3}0.159{col 55}{space 4} -8.36153{col 68}{space 3} 1.368364
{txt}{space 9}290  {c |}{col 15}{res}{space 2} 3.414241{col 27}{space 2} 3.775905{col 38}{space 1}    0.90{col 47}{space 3}0.366{col 55}{space 4}-3.986397{col 68}{space 3} 10.81488
{txt}{space 9}305  {c |}{col 15}{res}{space 2} 8.178241{col 27}{space 2} 7.933996{col 38}{space 1}    1.03{col 47}{space 3}0.303{col 55}{space 4}-7.372106{col 68}{space 3} 23.72859
{txt}{space 9}310  {c |}{col 15}{res}{space 2} 8.046485{col 27}{space 2} 7.856054{col 38}{space 1}    1.02{col 47}{space 3}0.306{col 55}{space 4}-7.351098{col 68}{space 3} 23.44407
{txt}{space 9}316  {c |}{col 15}{res}{space 2}   7.9922{col 27}{space 2} 7.467233{col 38}{space 1}    1.07{col 47}{space 3}0.284{col 55}{space 4}-6.643308{col 68}{space 3} 22.62771
{txt}{space 9}317  {c |}{col 15}{res}{space 2} 8.642183{col 27}{space 2} 8.637597{col 38}{space 1}    1.00{col 47}{space 3}0.317{col 55}{space 4}-8.287196{col 68}{space 3} 25.57156
{txt}{space 9}325  {c |}{col 15}{res}{space 2}-.7006711{col 27}{space 2} .7511693{col 38}{space 1}   -0.93{col 47}{space 3}0.351{col 55}{space 4}-2.172936{col 68}{space 3} .7715937
{txt}{space 9}338  {c |}{col 15}{res}{space 2} 8.903515{col 27}{space 2} 9.129199{col 38}{space 1}    0.98{col 47}{space 3}0.329{col 55}{space 4}-8.989386{col 68}{space 3} 26.79642
{txt}{space 9}349  {c |}{col 15}{res}{space 2} 8.670721{col 27}{space 2} 8.754034{col 38}{space 1}    0.99{col 47}{space 3}0.322{col 55}{space 4} -8.48687{col 68}{space 3} 25.82831
{txt}{space 9}350  {c |}{col 15}{res}{space 2}  7.77399{col 27}{space 2}  7.91443{col 38}{space 1}    0.98{col 47}{space 3}0.326{col 55}{space 4}-7.738008{col 68}{space 3} 23.28599
{txt}{space 9}352  {c |}{col 15}{res}{space 2} 9.724069{col 27}{space 2} 9.074341{col 38}{space 1}    1.07{col 47}{space 3}0.284{col 55}{space 4}-8.061312{col 68}{space 3} 27.50945
{txt}{space 9}355  {c |}{col 15}{res}{space 2} 7.438462{col 27}{space 2} 8.287316{col 38}{space 1}    0.90{col 47}{space 3}0.369{col 55}{space 4} -8.80438{col 68}{space 3}  23.6813
{txt}{space 9}360  {c |}{col 15}{res}{space 2} 9.032902{col 27}{space 2} 6.588709{col 38}{space 1}    1.37{col 47}{space 3}0.170{col 55}{space 4}-3.880731{col 68}{space 3} 21.94653
{txt}{space 9}366  {c |}{col 15}{res}{space 2} 9.123385{col 27}{space 2} 9.062985{col 38}{space 1}    1.01{col 47}{space 3}0.314{col 55}{space 4}-8.639739{col 68}{space 3} 26.88651
{txt}{space 9}367  {c |}{col 15}{res}{space 2} 9.522936{col 27}{space 2} 8.904516{col 38}{space 1}    1.07{col 47}{space 3}0.285{col 55}{space 4}-7.929594{col 68}{space 3} 26.97547
{txt}{space 9}368  {c |}{col 15}{res}{space 2} 8.429407{col 27}{space 2} 8.945739{col 38}{space 1}    0.94{col 47}{space 3}0.346{col 55}{space 4}-9.103919{col 68}{space 3} 25.96273
{txt}{space 9}375  {c |}{col 15}{res}{space 2} 10.73548{col 27}{space 2} 8.374503{col 38}{space 1}    1.28{col 47}{space 3}0.200{col 55}{space 4} -5.67824{col 68}{space 3} 27.14921
{txt}{space 9}380  {c |}{col 15}{res}{space 2} 10.48101{col 27}{space 2} 8.192398{col 38}{space 1}    1.28{col 47}{space 3}0.201{col 55}{space 4}-5.575798{col 68}{space 3} 26.53781
{txt}{space 9}390  {c |}{col 15}{res}{space 2} 10.14416{col 27}{space 2} 8.389134{col 38}{space 1}    1.21{col 47}{space 3}0.227{col 55}{space 4} -6.29824{col 68}{space 3} 26.58656
{txt}{space 13} {c |}
{space 8}party {c |}
{space 9}EPP  {c |}{col 15}{res}{space 2} 1.950616{col 27}{space 2} 1.002245{col 38}{space 1}    1.95{col 47}{space 3}0.052{col 55}{space 4} -.013749{col 68}{space 3} 3.914981
{txt}{space 9}S&D  {c |}{col 15}{res}{space 2} 1.961994{col 27}{space 2} 1.018522{col 38}{space 1}    1.93{col 47}{space 3}0.054{col 55}{space 4} -.034273{col 68}{space 3} 3.958261
{txt}{space 2}Greens/EFA  {c |}{col 15}{res}{space 2}-.8506557{col 27}{space 2} .9870725{col 38}{space 1}   -0.86{col 47}{space 3}0.389{col 55}{space 4}-2.785282{col 68}{space 3} 1.083971
{txt}{space 3}ALDE/ADLE  {c |}{col 15}{res}{space 2} 2.102328{col 27}{space 2} 1.039625{col 38}{space 1}    2.02{col 47}{space 3}0.043{col 55}{space 4} .0646994{col 68}{space 3} 4.139956
{txt}{space 9}ECR  {c |}{col 15}{res}{space 2} 3.910103{col 27}{space 2} 1.004785{col 38}{space 1}    3.89{col 47}{space 3}0.000{col 55}{space 4} 1.940761{col 68}{space 3} 5.879445
{txt}{space 8}EFDD  {c |}{col 15}{res}{space 2}-.2116836{col 27}{space 2} .9357962{col 38}{space 1}   -0.23{col 47}{space 3}0.821{col 55}{space 4} -2.04581{col 68}{space 3} 1.622443
{txt}{space 5}GUE-NGL  {c |}{col 15}{res}{space 2}-.8227307{col 27}{space 2} 1.290827{col 38}{space 1}   -0.64{col 47}{space 3}0.524{col 55}{space 4}-3.352704{col 68}{space 3} 1.707243
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2}-10.63124{col 27}{space 2} 9.110995{col 38}{space 1}   -1.17{col 47}{space 3}0.243{col 55}{space 4}-28.48846{col 68}{space 3} 7.225983
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA36, title((A36))
{txt}
{com}. 
. estout MODELA26 MODELA27 MODELA28 MODELA29 MODELA30 MODELA31 MODELA32 MODELA33 MODELA34 MODELA35 MODELA36, cells(b(star fmt(3)) se(par fmt(3))) starlevels($^{c -(}+{c )-}$ 0.10 $^{c -(}*{c )-}$ 0.05 $^{c -(}**{c )-}$ 0.01 $^{c -(}***{c )-}$ 0.001) stats(N N_g r2, fmt(0 0 3) label(Observations Countries R$^2$)) label,  using "modelsa26_a36.tex", replace style(tex)
{res}{txt}(note: file modelsa26_a36.tex not found)
(output written to {browse  `"modelsa26_a36.tex"'})

{com}. 
. **TABLE A6
. bysort ccode bill: egen avgprosanctionsvote = mean(prosanctions)
{txt}
{com}. collapse avgprosanctionsvote loggedmigrants population unemployment gdpgrowth distance col immigrantpolicy RWPvoteshare exportsGDP importsGDP pincometax sstax logged_refugee lnremit, by(ccode bill)
{txt}
{com}. //Model A37
. fracreg logit avgprosanctionsvote loggedmigrants

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-56.534024}  
Iteration 1:{space 3}log pseudolikelihood = {res:-55.393014}  
Iteration 2:{space 3}log pseudolikelihood = {res:-55.391453}  
Iteration 3:{space 3}log pseudolikelihood = {res:-55.391453}  
{res}
{txt}Fractional logistic regression{col 49}Number of obs{col 67}= {res}        83
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      3.98
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0461
{txt}Log pseudolikelihood = {res}-55.391453{txt}{col 49}Pseudo R2{col 67}= {res}    0.0170

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}avgprosanctionsvote{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}loggedmigrants {c |}{col 21}{res}{space 2}-.1013792{col 33}{space 2} .0508239{col 44}{space 1}   -1.99{col 53}{space 3}0.046{col 61}{space 4}-.2009921{col 74}{space 3}-.0017663
{txt}{space 14}_cons {c |}{col 21}{res}{space 2} .9578517{col 33}{space 2} .3744457{col 44}{space 1}    2.56{col 53}{space 3}0.011{col 61}{space 4} .2239516{col 74}{space 3} 1.691752
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA37, title((A37))
{txt}
{com}. //Model A38
. fracreg logit avgprosanctionsvote loggedmigrants i.bill

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-50.063746}  
Iteration 1:{space 3}log pseudolikelihood = {res: -39.28041}  
Iteration 2:{space 3}log pseudolikelihood = {res:-39.206319}  
Iteration 3:{space 3}log pseudolikelihood = {res:-39.206087}  
Iteration 4:{space 3}log pseudolikelihood = {res:-39.206087}  
{res}
{txt}Fractional logistic regression{col 49}Number of obs{col 67}= {res}        83
{txt}{col 49}Wald chi2({res}3{txt}){col 67}= {res}    159.62
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-39.206087{txt}{col 49}Pseudo R2{col 67}= {res}    0.3042

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}avgprosanctionsvote{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}loggedmigrants {c |}{col 21}{res}{space 2}-.0981566{col 33}{space 2} .0346078{col 44}{space 1}   -2.84{col 53}{space 3}0.005{col 61}{space 4}-.1659866{col 74}{space 3}-.0303266
{txt}{space 19} {c |}
{space 15}bill {c |}
{space 17}2  {c |}{col 21}{res}{space 2} 3.273898{col 33}{space 2} .2733025{col 44}{space 1}   11.98{col 53}{space 3}0.000{col 61}{space 4} 2.738235{col 74}{space 3} 3.809561
{txt}{space 17}3  {c |}{col 21}{res}{space 2} 2.885104{col 33}{space 2} .3029927{col 44}{space 1}    9.52{col 53}{space 3}0.000{col 61}{space 4}  2.29125{col 74}{space 3} 3.478959
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-1.134722{col 33}{space 2} .2916591{col 44}{space 1}   -3.89{col 53}{space 3}0.000{col 61}{space 4}-1.706364{col 74}{space 3} -.563081
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA38, title((A38))
{txt}
{com}. //Model A39
. fracreg logit avgprosanctionsvote loggedmigrants i.bill i.ccode

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-49.600658}  
Iteration 1:{space 3}log pseudolikelihood = {res:-37.577172}  
Iteration 2:{space 3}log pseudolikelihood = {res:-37.451471}  
Iteration 3:{space 3}log pseudolikelihood = {res:-37.450667}  
Iteration 4:{space 3}log pseudolikelihood = {res:-37.450666}  
{res}
{txt}Fractional logistic regression{col 49}Number of obs{col 67}= {res}        83
{txt}{col 49}Wald chi2({res}30{txt}){col 67}= {res}    254.22
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-37.450666{txt}{col 49}Pseudo R2{col 67}= {res}    0.3354

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}avgprosanctionsvote{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}loggedmigrants {c |}{col 21}{res}{space 2}-.1430995{col 33}{space 2}  .063142{col 44}{space 1}   -2.27{col 53}{space 3}0.023{col 61}{space 4}-.2668554{col 74}{space 3}-.0193435
{txt}{space 19} {c |}
{space 15}bill {c |}
{space 17}2  {c |}{col 21}{res}{space 2} 3.450004{col 33}{space 2}  .302282{col 44}{space 1}   11.41{col 53}{space 3}0.000{col 61}{space 4} 2.857542{col 74}{space 3} 4.042466
{txt}{space 17}3  {c |}{col 21}{res}{space 2} 2.927985{col 33}{space 2} .2822425{col 44}{space 1}   10.37{col 53}{space 3}0.000{col 61}{space 4}   2.3748{col 74}{space 3}  3.48117
{txt}{space 19} {c |}
{space 14}ccode {c |}
{space 15}205  {c |}{col 21}{res}{space 2}-1.767439{col 33}{space 2} .3658044{col 44}{space 1}   -4.83{col 53}{space 3}0.000{col 61}{space 4}-2.484402{col 74}{space 3}-1.050475
{txt}{space 15}210  {c |}{col 21}{res}{space 2}-.1755558{col 33}{space 2} .3940888{col 44}{space 1}   -0.45{col 53}{space 3}0.656{col 61}{space 4}-.9479556{col 74}{space 3}  .596844
{txt}{space 15}211  {c |}{col 21}{res}{space 2}-1.197924{col 33}{space 2} .4590294{col 44}{space 1}   -2.61{col 53}{space 3}0.009{col 61}{space 4}-2.097605{col 74}{space 3} -.298243
{txt}{space 15}212  {c |}{col 21}{res}{space 2}-1.429142{col 33}{space 2} .5702322{col 44}{space 1}   -2.51{col 53}{space 3}0.012{col 61}{space 4}-2.546776{col 74}{space 3}-.3115069
{txt}{space 15}220  {c |}{col 21}{res}{space 2}-1.440106{col 33}{space 2} .3211299{col 44}{space 1}   -4.48{col 53}{space 3}0.000{col 61}{space 4}-2.069509{col 74}{space 3}-.8107028
{txt}{space 15}230  {c |}{col 21}{res}{space 2}-1.184291{col 33}{space 2} .5268917{col 44}{space 1}   -2.25{col 53}{space 3}0.025{col 61}{space 4} -2.21698{col 74}{space 3}-.1516021
{txt}{space 15}235  {c |}{col 21}{res}{space 2}-.8610034{col 33}{space 2} .6126182{col 44}{space 1}   -1.41{col 53}{space 3}0.160{col 61}{space 4}-2.061713{col 74}{space 3} .3397062
{txt}{space 15}255  {c |}{col 21}{res}{space 2} -.618249{col 33}{space 2} .3910524{col 44}{space 1}   -1.58{col 53}{space 3}0.114{col 61}{space 4}-1.384698{col 74}{space 3} .1481996
{txt}{space 15}290  {c |}{col 21}{res}{space 2}-.4933771{col 33}{space 2} .6985542{col 44}{space 1}   -0.71{col 53}{space 3}0.480{col 61}{space 4}-1.862518{col 74}{space 3}  .875764
{txt}{space 15}305  {c |}{col 21}{res}{space 2}-1.099819{col 33}{space 2} .7436098{col 44}{space 1}   -1.48{col 53}{space 3}0.139{col 61}{space 4}-2.557268{col 74}{space 3} .3576291
{txt}{space 15}310  {c |}{col 21}{res}{space 2} -.687548{col 33}{space 2} .4235972{col 44}{space 1}   -1.62{col 53}{space 3}0.105{col 61}{space 4}-1.517783{col 74}{space 3} .1426873
{txt}{space 15}316  {c |}{col 21}{res}{space 2} -.313564{col 33}{space 2} .3833767{col 44}{space 1}   -0.82{col 53}{space 3}0.413{col 61}{space 4}-1.064969{col 74}{space 3} .4378406
{txt}{space 15}317  {c |}{col 21}{res}{space 2}-.7339103{col 33}{space 2} .7398383{col 44}{space 1}   -0.99{col 53}{space 3}0.321{col 61}{space 4}-2.183967{col 74}{space 3} .7161461
{txt}{space 15}325  {c |}{col 21}{res}{space 2}-.7282048{col 33}{space 2}  .316808{col 44}{space 1}   -2.30{col 53}{space 3}0.022{col 61}{space 4}-1.349137{col 74}{space 3}-.1072724
{txt}{space 15}338  {c |}{col 21}{res}{space 2}-.3318448{col 33}{space 2} .8855952{col 44}{space 1}   -0.37{col 53}{space 3}0.708{col 61}{space 4} -2.06758{col 74}{space 3}  1.40389
{txt}{space 15}344  {c |}{col 21}{res}{space 2}-.0716787{col 33}{space 2} .8861018{col 44}{space 1}   -0.08{col 53}{space 3}0.936{col 61}{space 4}-1.808406{col 74}{space 3} 1.665049
{txt}{space 15}349  {c |}{col 21}{res}{space 2}-.9004317{col 33}{space 2} .7142624{col 44}{space 1}   -1.26{col 53}{space 3}0.207{col 61}{space 4} -2.30036{col 74}{space 3}  .499497
{txt}{space 15}350  {c |}{col 21}{res}{space 2}-2.341746{col 33}{space 2} .9268358{col 44}{space 1}   -2.53{col 53}{space 3}0.012{col 61}{space 4} -4.15831{col 74}{space 3}-.5251808
{txt}{space 15}352  {c |}{col 21}{res}{space 2} -1.21183{col 33}{space 2} .4975242{col 44}{space 1}   -2.44{col 53}{space 3}0.015{col 61}{space 4} -2.18696{col 74}{space 3}-.2367004
{txt}{space 15}355  {c |}{col 21}{res}{space 2}-.6236081{col 33}{space 2} .6887015{col 44}{space 1}   -0.91{col 53}{space 3}0.365{col 61}{space 4}-1.973438{col 74}{space 3} .7262221
{txt}{space 15}360  {c |}{col 21}{res}{space 2} -.028641{col 33}{space 2} .7487047{col 44}{space 1}   -0.04{col 53}{space 3}0.969{col 61}{space 4}-1.496075{col 74}{space 3} 1.438793
{txt}{space 15}366  {c |}{col 21}{res}{space 2}-1.532491{col 33}{space 2} .8198181{col 44}{space 1}   -1.87{col 53}{space 3}0.062{col 61}{space 4}-3.139305{col 74}{space 3} .0743232
{txt}{space 15}367  {c |}{col 21}{res}{space 2}-1.210239{col 33}{space 2} .6457747{col 44}{space 1}   -1.87{col 53}{space 3}0.061{col 61}{space 4}-2.475934{col 74}{space 3} .0554563
{txt}{space 15}368  {c |}{col 21}{res}{space 2}-1.117494{col 33}{space 2} .8308906{col 44}{space 1}   -1.34{col 53}{space 3}0.179{col 61}{space 4}-2.746009{col 74}{space 3} .5110221
{txt}{space 15}375  {c |}{col 21}{res}{space 2}-.1125468{col 33}{space 2} .5048462{col 44}{space 1}   -0.22{col 53}{space 3}0.824{col 61}{space 4}-1.102027{col 74}{space 3} .8769336
{txt}{space 15}380  {c |}{col 21}{res}{space 2}-.9220214{col 33}{space 2} 1.022045{col 44}{space 1}   -0.90{col 53}{space 3}0.367{col 61}{space 4}-2.925192{col 74}{space 3} 1.081149
{txt}{space 15}390  {c |}{col 21}{res}{space 2}-.7127777{col 33}{space 2} .5725692{col 44}{space 1}   -1.24{col 53}{space 3}0.213{col 61}{space 4}-1.834993{col 74}{space 3} .4094372
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} -.042093{col 33}{space 2} .7118275{col 44}{space 1}   -0.06{col 53}{space 3}0.953{col 61}{space 4}-1.437249{col 74}{space 3} 1.353063
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA39, title((A39))
{txt}
{com}. //Model A40
. fracreg logit avgprosanctionsvote loggedmigrants population unemployment gdpgrowth distance col i.bill i.ccode

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-49.484213}  
Iteration 1:{space 3}log pseudolikelihood = {res:-37.351207}  
Iteration 2:{space 3}log pseudolikelihood = {res: -37.20381}  
Iteration 3:{space 3}log pseudolikelihood = {res:-37.202575}  
Iteration 4:{space 3}log pseudolikelihood = {res:-37.202574}  
{res}
{txt}Fractional logistic regression{col 49}Number of obs{col 67}= {res}        83
{txt}{col 49}Wald chi2({res}35{txt}){col 67}= {res}    380.92
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-37.202574{txt}{col 49}Pseudo R2{col 67}= {res}    0.3398

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}avgprosanctionsvote{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}loggedmigrants {c |}{col 21}{res}{space 2}  -.17629{col 33}{space 2} .0721527{col 44}{space 1}   -2.44{col 53}{space 3}0.015{col 61}{space 4}-.3177067{col 74}{space 3}-.0348734
{txt}{space 9}population {c |}{col 21}{res}{space 2} .0997989{col 33}{space 2} .2683948{col 44}{space 1}    0.37{col 53}{space 3}0.710{col 61}{space 4}-.4262453{col 74}{space 3} .6258432
{txt}{space 7}unemployment {c |}{col 21}{res}{space 2} -.104801{col 33}{space 2} .0539248{col 44}{space 1}   -1.94{col 53}{space 3}0.052{col 61}{space 4}-.2104916{col 74}{space 3} .0008897
{txt}{space 10}gdpgrowth {c |}{col 21}{res}{space 2} .0283302{col 33}{space 2} .1028885{col 44}{space 1}    0.28{col 53}{space 3}0.783{col 61}{space 4}-.1733275{col 74}{space 3} .2299879
{txt}{space 11}distance {c |}{col 21}{res}{space 2} .0001615{col 33}{space 2} .0002396{col 44}{space 1}    0.67{col 53}{space 3}0.500{col 61}{space 4}-.0003081{col 74}{space 3} .0006311
{txt}{space 16}col {c |}{col 21}{res}{space 2} .5446956{col 33}{space 2} .4284489{col 44}{space 1}    1.27{col 53}{space 3}0.204{col 61}{space 4}-.2950487{col 74}{space 3}  1.38444
{txt}{space 19} {c |}
{space 15}bill {c |}
{space 17}2  {c |}{col 21}{res}{space 2} 3.437895{col 33}{space 2} .3858357{col 44}{space 1}    8.91{col 53}{space 3}0.000{col 61}{space 4} 2.681671{col 74}{space 3} 4.194119
{txt}{space 17}3  {c |}{col 21}{res}{space 2} 2.927348{col 33}{space 2} .3139479{col 44}{space 1}    9.32{col 53}{space 3}0.000{col 61}{space 4} 2.312021{col 74}{space 3} 3.542675
{txt}{space 19} {c |}
{space 14}ccode {c |}
{space 15}205  {c |}{col 21}{res}{space 2} 4.484205{col 33}{space 2} 15.88867{col 44}{space 1}    0.28{col 53}{space 3}0.778{col 61}{space 4}-26.65701{col 74}{space 3} 35.62542
{txt}{space 15}210  {c |}{col 21}{res}{space 2} 4.495953{col 33}{space 2} 12.62144{col 44}{space 1}    0.36{col 53}{space 3}0.722{col 61}{space 4}-20.24161{col 74}{space 3} 29.23352
{txt}{space 15}211  {c |}{col 21}{res}{space 2} 4.099541{col 33}{space 2}  14.0465{col 44}{space 1}    0.29{col 53}{space 3}0.770{col 61}{space 4}-23.43109{col 74}{space 3} 31.63017
{txt}{space 15}212  {c |}{col 21}{res}{space 2} 4.458851{col 33}{space 2} 16.85461{col 44}{space 1}    0.26{col 53}{space 3}0.791{col 61}{space 4}-28.57559{col 74}{space 3} 37.49329
{txt}{space 15}220  {c |}{col 21}{res}{space 2}-1.339896{col 33}{space 2} .6461818{col 44}{space 1}   -2.07{col 53}{space 3}0.038{col 61}{space 4} -2.60639{col 74}{space 3}-.0734035
{txt}{space 15}230  {c |}{col 21}{res}{space 2}  2.25503{col 33}{space 2} 4.780041{col 44}{space 1}    0.47{col 53}{space 3}0.637{col 61}{space 4}-7.113679{col 74}{space 3} 11.62374
{txt}{space 15}235  {c |}{col 21}{res}{space 2} 4.956519{col 33}{space 2} 14.14841{col 44}{space 1}    0.35{col 53}{space 3}0.726{col 61}{space 4}-22.77385{col 74}{space 3} 32.68689
{txt}{space 15}255  {c |}{col 21}{res}{space 2}-2.401811{col 33}{space 2} 4.603608{col 44}{space 1}   -0.52{col 53}{space 3}0.602{col 61}{space 4}-11.42472{col 74}{space 3} 6.621095
{txt}{space 15}290  {c |}{col 21}{res}{space 2} 2.323159{col 33}{space 2} 7.004524{col 44}{space 1}    0.33{col 53}{space 3}0.740{col 61}{space 4}-11.40546{col 74}{space 3} 16.05177
{txt}{space 15}305  {c |}{col 21}{res}{space 2} 4.371584{col 33}{space 2} 14.91307{col 44}{space 1}    0.29{col 53}{space 3}0.769{col 61}{space 4} -24.8575{col 74}{space 3} 33.60067
{txt}{space 15}310  {c |}{col 21}{res}{space 2} 5.039463{col 33}{space 2} 14.56292{col 44}{space 1}    0.35{col 53}{space 3}0.729{col 61}{space 4}-23.50334{col 74}{space 3} 33.58227
{txt}{space 15}316  {c |}{col 21}{res}{space 2} 5.023523{col 33}{space 2}   14.294{col 44}{space 1}    0.35{col 53}{space 3}0.725{col 61}{space 4}-22.99219{col 74}{space 3} 33.03924
{txt}{space 15}317  {c |}{col 21}{res}{space 2} 5.789283{col 33}{space 2} 15.79967{col 44}{space 1}    0.37{col 53}{space 3}0.714{col 61}{space 4}-25.17751{col 74}{space 3} 36.75608
{txt}{space 15}325  {c |}{col 21}{res}{space 2} .1259122{col 33}{space 2} 1.230122{col 44}{space 1}    0.10{col 53}{space 3}0.918{col 61}{space 4}-2.285082{col 74}{space 3} 2.536906
{txt}{space 15}338  {c |}{col 21}{res}{space 2} 6.032233{col 33}{space 2} 17.12173{col 44}{space 1}    0.35{col 53}{space 3}0.725{col 61}{space 4}-27.52575{col 74}{space 3} 39.59022
{txt}{space 15}344  {c |}{col 21}{res}{space 2} 6.913268{col 33}{space 2}  15.9924{col 44}{space 1}    0.43{col 53}{space 3}0.666{col 61}{space 4}-24.43125{col 74}{space 3} 38.25779
{txt}{space 15}349  {c |}{col 21}{res}{space 2} 5.461841{col 33}{space 2} 16.61866{col 44}{space 1}    0.33{col 53}{space 3}0.742{col 61}{space 4}-27.11013{col 74}{space 3} 38.03381
{txt}{space 15}350  {c |}{col 21}{res}{space 2} 5.184777{col 33}{space 2} 14.42456{col 44}{space 1}    0.36{col 53}{space 3}0.719{col 61}{space 4}-23.08684{col 74}{space 3} 33.45639
{txt}{space 15}352  {c |}{col 21}{res}{space 2} 6.146538{col 33}{space 2} 16.99001{col 44}{space 1}    0.36{col 53}{space 3}0.718{col 61}{space 4}-27.15328{col 74}{space 3} 39.44635
{txt}{space 15}355  {c |}{col 21}{res}{space 2} 5.665007{col 33}{space 2} 15.26383{col 44}{space 1}    0.37{col 53}{space 3}0.711{col 61}{space 4}-24.25156{col 74}{space 3} 35.58157
{txt}{space 15}360  {c |}{col 21}{res}{space 2} 4.525326{col 33}{space 2} 11.84629{col 44}{space 1}    0.38{col 53}{space 3}0.702{col 61}{space 4}-18.69298{col 74}{space 3} 27.74363
{txt}{space 15}366  {c |}{col 21}{res}{space 2} 4.806601{col 33}{space 2} 16.60373{col 44}{space 1}    0.29{col 53}{space 3}0.772{col 61}{space 4}-27.73612{col 74}{space 3} 37.34932
{txt}{space 15}367  {c |}{col 21}{res}{space 2} 5.522229{col 33}{space 2} 16.52089{col 44}{space 1}    0.33{col 53}{space 3}0.738{col 61}{space 4}-26.85812{col 74}{space 3} 37.90258
{txt}{space 15}368  {c |}{col 21}{res}{space 2} 5.467362{col 33}{space 2} 16.30923{col 44}{space 1}    0.34{col 53}{space 3}0.737{col 61}{space 4}-26.49814{col 74}{space 3} 37.43286
{txt}{space 15}375  {c |}{col 21}{res}{space 2} 5.811037{col 33}{space 2} 15.63098{col 44}{space 1}    0.37{col 53}{space 3}0.710{col 61}{space 4}-24.82513{col 74}{space 3}  36.4472
{txt}{space 15}380  {c |}{col 21}{res}{space 2} 4.589375{col 33}{space 2} 14.44358{col 44}{space 1}    0.32{col 53}{space 3}0.751{col 61}{space 4}-23.71952{col 74}{space 3} 32.89827
{txt}{space 15}390  {c |}{col 21}{res}{space 2} 5.089869{col 33}{space 2} 15.59869{col 44}{space 1}    0.33{col 53}{space 3}0.744{col 61}{space 4}  -25.483{col 74}{space 3} 35.66274
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-5.957248{col 33}{space 2} 17.03942{col 44}{space 1}   -0.35{col 53}{space 3}0.727{col 61}{space 4} -39.3539{col 74}{space 3}  27.4394
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA40, title((A40))
{txt}
{com}. //Model A41
. fracreg logit avgprosanctionsvote loggedmigrants population unemployment gdpgrowth distance col immigrantpolicy i.bill

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-49.852391}  
Iteration 1:{space 3}log pseudolikelihood = {res:-38.590084}  
Iteration 2:{space 3}log pseudolikelihood = {res:-38.484878}  
Iteration 3:{space 3}log pseudolikelihood = {res:-38.484587}  
Iteration 4:{space 3}log pseudolikelihood = {res:-38.484587}  
{res}
{txt}Fractional logistic regression{col 49}Number of obs{col 67}= {res}        83
{txt}{col 49}Wald chi2({res}9{txt}){col 67}= {res}    264.51
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-38.484587{txt}{col 49}Pseudo R2{col 67}= {res}    0.3170

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}avgprosanctionsvote{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}loggedmigrants {c |}{col 21}{res}{space 2}-.1454944{col 33}{space 2} .0672519{col 44}{space 1}   -2.16{col 53}{space 3}0.031{col 61}{space 4}-.2773058{col 74}{space 3}-.0136831
{txt}{space 9}population {c |}{col 21}{res}{space 2} .0039857{col 33}{space 2} .0061818{col 44}{space 1}    0.64{col 53}{space 3}0.519{col 61}{space 4}-.0081305{col 74}{space 3} .0161018
{txt}{space 7}unemployment {c |}{col 21}{res}{space 2}-.0673706{col 33}{space 2} .0347325{col 44}{space 1}   -1.94{col 53}{space 3}0.052{col 61}{space 4}-.1354451{col 74}{space 3} .0007038
{txt}{space 10}gdpgrowth {c |}{col 21}{res}{space 2}-.0126528{col 33}{space 2} .0727102{col 44}{space 1}   -0.17{col 53}{space 3}0.862{col 61}{space 4}-.1551622{col 74}{space 3} .1298566
{txt}{space 11}distance {c |}{col 21}{res}{space 2}-.0000445{col 33}{space 2} .0001296{col 44}{space 1}   -0.34{col 53}{space 3}0.731{col 61}{space 4}-.0002986{col 74}{space 3} .0002096
{txt}{space 16}col {c |}{col 21}{res}{space 2} .0095454{col 33}{space 2} .3031689{col 44}{space 1}    0.03{col 53}{space 3}0.975{col 61}{space 4}-.5846547{col 74}{space 3} .6037456
{txt}{space 4}immigrantpolicy {c |}{col 21}{res}{space 2}-.1747162{col 33}{space 2} .5691264{col 44}{space 1}   -0.31{col 53}{space 3}0.759{col 61}{space 4}-1.290183{col 74}{space 3}  .940751
{txt}{space 19} {c |}
{space 15}bill {c |}
{space 17}2  {c |}{col 21}{res}{space 2} 3.449541{col 33}{space 2} .3195487{col 44}{space 1}   10.80{col 53}{space 3}0.000{col 61}{space 4} 2.823237{col 74}{space 3} 4.075845
{txt}{space 17}3  {c |}{col 21}{res}{space 2} 2.850959{col 33}{space 2} .2675136{col 44}{space 1}   10.66{col 53}{space 3}0.000{col 61}{space 4} 2.326642{col 74}{space 3} 3.375276
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} .3373235{col 33}{space 2} 1.490937{col 44}{space 1}    0.23{col 53}{space 3}0.821{col 61}{space 4}-2.584859{col 74}{space 3} 3.259506
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA41, title((A41))
{txt}
{com}. //Model A42
. fracreg logit avgprosanctionsvote loggedmigrants population unemployment gdpgrowth distance col RWPvoteshare i.bill i.ccode

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-49.384013}  
Iteration 1:{space 3}log pseudolikelihood = {res:-37.063784}  
Iteration 2:{space 3}log pseudolikelihood = {res:  -36.9232}  
Iteration 3:{space 3}log pseudolikelihood = {res: -36.92216}  
Iteration 4:{space 3}log pseudolikelihood = {res:-36.922159}  
{res}
{txt}Fractional logistic regression{col 49}Number of obs{col 67}= {res}        83
{txt}{col 49}Wald chi2({res}36{txt}){col 67}= {res}    586.54
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-36.922159{txt}{col 49}Pseudo R2{col 67}= {res}    0.3447

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}avgprosanctionsvote{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}loggedmigrants {c |}{col 21}{res}{space 2}-.1530794{col 33}{space 2} .0729363{col 44}{space 1}   -2.10{col 53}{space 3}0.036{col 61}{space 4}-.2960319{col 74}{space 3}-.0101269
{txt}{space 9}population {c |}{col 21}{res}{space 2} .0695041{col 33}{space 2} .2314362{col 44}{space 1}    0.30{col 53}{space 3}0.764{col 61}{space 4}-.3841025{col 74}{space 3} .5231106
{txt}{space 7}unemployment {c |}{col 21}{res}{space 2}-.1026707{col 33}{space 2} .0476034{col 44}{space 1}   -2.16{col 53}{space 3}0.031{col 61}{space 4}-.1959718{col 74}{space 3}-.0093697
{txt}{space 10}gdpgrowth {c |}{col 21}{res}{space 2} .0305727{col 33}{space 2}  .084831{col 44}{space 1}    0.36{col 53}{space 3}0.719{col 61}{space 4} -.135693{col 74}{space 3} .1968384
{txt}{space 11}distance {c |}{col 21}{res}{space 2} .0002245{col 33}{space 2} .0002269{col 44}{space 1}    0.99{col 53}{space 3}0.322{col 61}{space 4}-.0002202{col 74}{space 3} .0006692
{txt}{space 16}col {c |}{col 21}{res}{space 2} .0710304{col 33}{space 2} .3696734{col 44}{space 1}    0.19{col 53}{space 3}0.848{col 61}{space 4}-.6535162{col 74}{space 3} .7955769
{txt}{space 7}RWPvoteshare {c |}{col 21}{res}{space 2}-.0978614{col 33}{space 2} .0419407{col 44}{space 1}   -2.33{col 53}{space 3}0.020{col 61}{space 4}-.1800637{col 74}{space 3}-.0156592
{txt}{space 19} {c |}
{space 15}bill {c |}
{space 17}2  {c |}{col 21}{res}{space 2}   3.4418{col 33}{space 2} .3778946{col 44}{space 1}    9.11{col 53}{space 3}0.000{col 61}{space 4}  2.70114{col 74}{space 3}  4.18246
{txt}{space 17}3  {c |}{col 21}{res}{space 2} 3.147149{col 33}{space 2} .3467899{col 44}{space 1}    9.08{col 53}{space 3}0.000{col 61}{space 4} 2.467453{col 74}{space 3} 3.826845
{txt}{space 19} {c |}
{space 14}ccode {c |}
{space 15}205  {c |}{col 21}{res}{space 2} 2.710341{col 33}{space 2} 13.67624{col 44}{space 1}    0.20{col 53}{space 3}0.843{col 61}{space 4}-24.09459{col 74}{space 3} 29.51527
{txt}{space 15}210  {c |}{col 21}{res}{space 2} 4.320845{col 33}{space 2} 10.72024{col 44}{space 1}    0.40{col 53}{space 3}0.687{col 61}{space 4}-16.69044{col 74}{space 3} 25.33213
{txt}{space 15}211  {c |}{col 21}{res}{space 2}  3.26847{col 33}{space 2} 11.96885{col 44}{space 1}    0.27{col 53}{space 3}0.785{col 61}{space 4}-20.19004{col 74}{space 3} 26.72698
{txt}{space 15}212  {c |}{col 21}{res}{space 2} 2.757043{col 33}{space 2} 14.44791{col 44}{space 1}    0.19{col 53}{space 3}0.849{col 61}{space 4}-25.56034{col 74}{space 3} 31.07443
{txt}{space 15}220  {c |}{col 21}{res}{space 2} .0313684{col 33}{space 2} .8684485{col 44}{space 1}    0.04{col 53}{space 3}0.971{col 61}{space 4}-1.670759{col 74}{space 3} 1.733496
{txt}{space 15}230  {c |}{col 21}{res}{space 2} 1.804063{col 33}{space 2} 4.108011{col 44}{space 1}    0.44{col 53}{space 3}0.661{col 61}{space 4} -6.24749{col 74}{space 3} 9.855616
{txt}{space 15}235  {c |}{col 21}{res}{space 2} 3.456018{col 33}{space 2} 12.12264{col 44}{space 1}    0.29{col 53}{space 3}0.776{col 61}{space 4}-20.30392{col 74}{space 3} 27.21595
{txt}{space 15}255  {c |}{col 21}{res}{space 2} -1.48343{col 33}{space 2} 4.041942{col 44}{space 1}   -0.37{col 53}{space 3}0.714{col 61}{space 4} -9.40549{col 74}{space 3}  6.43863
{txt}{space 15}290  {c |}{col 21}{res}{space 2} 1.702283{col 33}{space 2}  6.03712{col 44}{space 1}    0.28{col 53}{space 3}0.778{col 61}{space 4}-10.13025{col 74}{space 3} 13.53482
{txt}{space 15}305  {c |}{col 21}{res}{space 2} 5.224882{col 33}{space 2} 12.57818{col 44}{space 1}    0.42{col 53}{space 3}0.678{col 61}{space 4}-19.42791{col 74}{space 3} 29.87767
{txt}{space 15}310  {c |}{col 21}{res}{space 2} 5.317969{col 33}{space 2} 12.33745{col 44}{space 1}    0.43{col 53}{space 3}0.666{col 61}{space 4}  -18.863{col 74}{space 3} 29.49893
{txt}{space 15}316  {c |}{col 21}{res}{space 2} 4.249711{col 33}{space 2} 12.19212{col 44}{space 1}    0.35{col 53}{space 3}0.727{col 61}{space 4} -19.6464{col 74}{space 3} 28.14582
{txt}{space 15}317  {c |}{col 21}{res}{space 2} 4.841366{col 33}{space 2} 13.51575{col 44}{space 1}    0.36{col 53}{space 3}0.720{col 61}{space 4}-21.64901{col 74}{space 3} 31.33174
{txt}{space 15}325  {c |}{col 21}{res}{space 2} .7815292{col 33}{space 2} .9876081{col 44}{space 1}    0.79{col 53}{space 3}0.429{col 61}{space 4}-1.154147{col 74}{space 3} 2.717205
{txt}{space 15}338  {c |}{col 21}{res}{space 2} 4.323342{col 33}{space 2} 14.74023{col 44}{space 1}    0.29{col 53}{space 3}0.769{col 61}{space 4}-24.56699{col 74}{space 3} 33.21367
{txt}{space 15}344  {c |}{col 21}{res}{space 2} 5.929847{col 33}{space 2} 13.63381{col 44}{space 1}    0.43{col 53}{space 3}0.664{col 61}{space 4}-20.79193{col 74}{space 3} 32.65162
{txt}{space 15}349  {c |}{col 21}{res}{space 2} 4.088761{col 33}{space 2} 14.24084{col 44}{space 1}    0.29{col 53}{space 3}0.774{col 61}{space 4}-23.82277{col 74}{space 3}  32.0003
{txt}{space 15}350  {c |}{col 21}{res}{space 2} 4.999911{col 33}{space 2} 12.26328{col 44}{space 1}    0.41{col 53}{space 3}0.683{col 61}{space 4}-19.03568{col 74}{space 3}  29.0355
{txt}{space 15}352  {c |}{col 21}{res}{space 2}   4.4519{col 33}{space 2}  14.6367{col 44}{space 1}    0.30{col 53}{space 3}0.761{col 61}{space 4}-24.23551{col 74}{space 3} 33.13931
{txt}{space 15}355  {c |}{col 21}{res}{space 2} 5.203402{col 33}{space 2} 13.00204{col 44}{space 1}    0.40{col 53}{space 3}0.689{col 61}{space 4}-20.28013{col 74}{space 3} 30.68694
{txt}{space 15}360  {c |}{col 21}{res}{space 2} 3.572475{col 33}{space 2}  10.1719{col 44}{space 1}    0.35{col 53}{space 3}0.725{col 61}{space 4}-16.36408{col 74}{space 3} 23.50903
{txt}{space 15}366  {c |}{col 21}{res}{space 2} 3.157994{col 33}{space 2} 14.20859{col 44}{space 1}    0.22{col 53}{space 3}0.824{col 61}{space 4}-24.69033{col 74}{space 3} 31.00632
{txt}{space 15}367  {c |}{col 21}{res}{space 2}   5.1571{col 33}{space 2} 14.02004{col 44}{space 1}    0.37{col 53}{space 3}0.713{col 61}{space 4}-22.32167{col 74}{space 3} 32.63587
{txt}{space 15}368  {c |}{col 21}{res}{space 2} 3.844491{col 33}{space 2} 13.99428{col 44}{space 1}    0.27{col 53}{space 3}0.784{col 61}{space 4}-23.58379{col 74}{space 3} 31.27277
{txt}{space 15}375  {c |}{col 21}{res}{space 2} 5.352984{col 33}{space 2} 13.26971{col 44}{space 1}    0.40{col 53}{space 3}0.687{col 61}{space 4}-20.65516{col 74}{space 3} 31.36113
{txt}{space 15}380  {c |}{col 21}{res}{space 2} 3.825039{col 33}{space 2} 12.23374{col 44}{space 1}    0.31{col 53}{space 3}0.755{col 61}{space 4}-20.15266{col 74}{space 3} 27.80273
{txt}{space 15}390  {c |}{col 21}{res}{space 2} 4.648909{col 33}{space 2} 13.27321{col 44}{space 1}    0.35{col 53}{space 3}0.726{col 61}{space 4}-21.36611{col 74}{space 3} 30.66393
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-4.580921{col 33}{space 2} 14.56583{col 44}{space 1}   -0.31{col 53}{space 3}0.753{col 61}{space 4}-33.12942{col 74}{space 3} 23.96758
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA42, title((A42))
{txt}
{com}. //Model A43
. fracreg logit avgprosanctionsvote loggedmigrants population unemployment gdpgrowth distance col exportsGDP importsGDP i.bill i.ccode

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -47.47213}  
Iteration 1:{space 3}log pseudolikelihood = {res:-35.395321}  
Iteration 2:{space 3}log pseudolikelihood = {res:-35.254712}  
Iteration 3:{space 3}log pseudolikelihood = {res:-35.253259}  
Iteration 4:{space 3}log pseudolikelihood = {res:-35.253257}  
{res}
{txt}Fractional logistic regression{col 49}Number of obs{col 67}= {res}        80
{txt}{col 49}Wald chi2({res}36{txt}){col 67}= {res}    458.36
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-35.253257{txt}{col 49}Pseudo R2{col 67}= {res}    0.3509

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}avgprosanctionsvote{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}loggedmigrants {c |}{col 21}{res}{space 2}-.1944446{col 33}{space 2} .0829939{col 44}{space 1}   -2.34{col 53}{space 3}0.019{col 61}{space 4}-.3571096{col 74}{space 3}-.0317796
{txt}{space 9}population {c |}{col 21}{res}{space 2} .1852431{col 33}{space 2}  .223422{col 44}{space 1}    0.83{col 53}{space 3}0.407{col 61}{space 4}-.2526559{col 74}{space 3} .6231421
{txt}{space 7}unemployment {c |}{col 21}{res}{space 2}-.0722998{col 33}{space 2} .0538251{col 44}{space 1}   -1.34{col 53}{space 3}0.179{col 61}{space 4}-.1777951{col 74}{space 3} .0331956
{txt}{space 10}gdpgrowth {c |}{col 21}{res}{space 2} .0895692{col 33}{space 2} .1135494{col 44}{space 1}    0.79{col 53}{space 3}0.430{col 61}{space 4}-.1329835{col 74}{space 3} .3121218
{txt}{space 11}distance {c |}{col 21}{res}{space 2} .0001359{col 33}{space 2} .0002734{col 44}{space 1}    0.50{col 53}{space 3}0.619{col 61}{space 4}   -.0004{col 74}{space 3} .0006718
{txt}{space 16}col {c |}{col 21}{res}{space 2} .6796365{col 33}{space 2} .3599888{col 44}{space 1}    1.89{col 53}{space 3}0.059{col 61}{space 4}-.0259286{col 74}{space 3} 1.385202
{txt}{space 9}exportsGDP {c |}{col 21}{res}{space 2} 211.2475{col 33}{space 2} 165.2422{col 44}{space 1}    1.28{col 53}{space 3}0.201{col 61}{space 4}-112.6212{col 74}{space 3} 535.1163
{txt}{space 9}importsGDP {c |}{col 21}{res}{space 2}-940.5121{col 33}{space 2}  422.976{col 44}{space 1}   -2.22{col 53}{space 3}0.026{col 61}{space 4} -1769.53{col 74}{space 3}-111.4945
{txt}{space 19} {c |}
{space 15}bill {c |}
{space 17}2  {c |}{col 21}{res}{space 2} 3.348635{col 33}{space 2} .4114729{col 44}{space 1}    8.14{col 53}{space 3}0.000{col 61}{space 4} 2.542163{col 74}{space 3} 4.155107
{txt}{space 17}3  {c |}{col 21}{res}{space 2} 3.058972{col 33}{space 2} .3320911{col 44}{space 1}    9.21{col 53}{space 3}0.000{col 61}{space 4} 2.408085{col 74}{space 3} 3.709858
{txt}{space 19} {c |}
{space 14}ccode {c |}
{space 15}205  {c |}{col 21}{res}{space 2} 9.700536{col 33}{space 2} 13.21447{col 44}{space 1}    0.73{col 53}{space 3}0.463{col 61}{space 4}-16.19936{col 74}{space 3} 35.60043
{txt}{space 15}210  {c |}{col 21}{res}{space 2} 8.990926{col 33}{space 2} 10.51856{col 44}{space 1}    0.85{col 53}{space 3}0.393{col 61}{space 4}-11.62508{col 74}{space 3} 29.60693
{txt}{space 15}211  {c |}{col 21}{res}{space 2} 8.544692{col 33}{space 2} 11.70181{col 44}{space 1}    0.73{col 53}{space 3}0.465{col 61}{space 4}-14.39043{col 74}{space 3} 31.47981
{txt}{space 15}220  {c |}{col 21}{res}{space 2}-1.420288{col 33}{space 2} .5562799{col 44}{space 1}   -2.55{col 53}{space 3}0.011{col 61}{space 4}-2.510577{col 74}{space 3}-.3299995
{txt}{space 15}230  {c |}{col 21}{res}{space 2} 3.380548{col 33}{space 2} 4.064177{col 44}{space 1}    0.83{col 53}{space 3}0.406{col 61}{space 4}-4.585093{col 74}{space 3} 11.34619
{txt}{space 15}235  {c |}{col 21}{res}{space 2} 9.265042{col 33}{space 2} 11.78984{col 44}{space 1}    0.79{col 53}{space 3}0.432{col 61}{space 4}-13.84261{col 74}{space 3} 32.37269
{txt}{space 15}255  {c |}{col 21}{res}{space 2} -3.67093{col 33}{space 2} 3.856386{col 44}{space 1}   -0.95{col 53}{space 3}0.341{col 61}{space 4}-11.22931{col 74}{space 3} 3.887448
{txt}{space 15}290  {c |}{col 21}{res}{space 2} 4.259288{col 33}{space 2} 5.893852{col 44}{space 1}    0.72{col 53}{space 3}0.470{col 61}{space 4} -7.29245{col 74}{space 3} 15.81103
{txt}{space 15}305  {c |}{col 21}{res}{space 2} 9.659991{col 33}{space 2} 12.45296{col 44}{space 1}    0.78{col 53}{space 3}0.438{col 61}{space 4}-14.74736{col 74}{space 3} 34.06734
{txt}{space 15}310  {c |}{col 21}{res}{space 2} 9.424158{col 33}{space 2} 12.12364{col 44}{space 1}    0.78{col 53}{space 3}0.437{col 61}{space 4}-14.33774{col 74}{space 3} 33.18606
{txt}{space 15}316  {c |}{col 21}{res}{space 2} 9.528426{col 33}{space 2} 11.90575{col 44}{space 1}    0.80{col 53}{space 3}0.424{col 61}{space 4}-13.80642{col 74}{space 3} 32.86327
{txt}{space 15}317  {c |}{col 21}{res}{space 2} 10.32695{col 33}{space 2} 13.19317{col 44}{space 1}    0.78{col 53}{space 3}0.434{col 61}{space 4}-15.53118{col 74}{space 3} 36.18508
{txt}{space 15}325  {c |}{col 21}{res}{space 2} .6800644{col 33}{space 2} 1.093662{col 44}{space 1}    0.62{col 53}{space 3}0.534{col 61}{space 4}-1.463474{col 74}{space 3} 2.823602
{txt}{space 15}338  {c |}{col 21}{res}{space 2} 10.75563{col 33}{space 2} 14.13349{col 44}{space 1}    0.76{col 53}{space 3}0.447{col 61}{space 4} -16.9455{col 74}{space 3} 38.45677
{txt}{space 15}344  {c |}{col 21}{res}{space 2} 11.96962{col 33}{space 2} 13.36411{col 44}{space 1}    0.90{col 53}{space 3}0.370{col 61}{space 4}-14.22355{col 74}{space 3}  38.1628
{txt}{space 15}349  {c |}{col 21}{res}{space 2} 10.47036{col 33}{space 2} 13.88452{col 44}{space 1}    0.75{col 53}{space 3}0.451{col 61}{space 4}-16.74279{col 74}{space 3} 37.68352
{txt}{space 15}350  {c |}{col 21}{res}{space 2} 10.13224{col 33}{space 2} 12.13904{col 44}{space 1}    0.83{col 53}{space 3}0.404{col 61}{space 4}-13.65984{col 74}{space 3} 33.92432
{txt}{space 15}352  {c |}{col 21}{res}{space 2} 11.48777{col 33}{space 2} 14.26527{col 44}{space 1}    0.81{col 53}{space 3}0.421{col 61}{space 4}-16.47165{col 74}{space 3} 39.44719
{txt}{space 15}355  {c |}{col 21}{res}{space 2} 10.45295{col 33}{space 2} 12.69227{col 44}{space 1}    0.82{col 53}{space 3}0.410{col 61}{space 4}-14.42344{col 74}{space 3} 35.32934
{txt}{space 15}360  {c |}{col 21}{res}{space 2} 8.084744{col 33}{space 2} 9.876245{col 44}{space 1}    0.82{col 53}{space 3}0.413{col 61}{space 4}-11.27234{col 74}{space 3} 27.44183
{txt}{space 15}366  {c |}{col 21}{res}{space 2} 9.801432{col 33}{space 2}  13.8143{col 44}{space 1}    0.71{col 53}{space 3}0.478{col 61}{space 4}-17.27409{col 74}{space 3} 36.87696
{txt}{space 15}367  {c |}{col 21}{res}{space 2} 10.24112{col 33}{space 2} 13.75413{col 44}{space 1}    0.74{col 53}{space 3}0.457{col 61}{space 4}-16.71649{col 74}{space 3} 37.19873
{txt}{space 15}368  {c |}{col 21}{res}{space 2}  9.95715{col 33}{space 2} 13.50637{col 44}{space 1}    0.74{col 53}{space 3}0.461{col 61}{space 4}-16.51485{col 74}{space 3} 36.42915
{txt}{space 15}375  {c |}{col 21}{res}{space 2} 10.70253{col 33}{space 2} 13.03146{col 44}{space 1}    0.82{col 53}{space 3}0.411{col 61}{space 4}-14.83867{col 74}{space 3} 36.24373
{txt}{space 15}380  {c |}{col 21}{res}{space 2} 9.103274{col 33}{space 2} 12.05466{col 44}{space 1}    0.76{col 53}{space 3}0.450{col 61}{space 4}-14.52343{col 74}{space 3} 32.72998
{txt}{space 15}390  {c |}{col 21}{res}{space 2} 10.05984{col 33}{space 2} 13.00694{col 44}{space 1}    0.77{col 53}{space 3}0.439{col 61}{space 4} -15.4333{col 74}{space 3} 35.55297
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-11.48925{col 33}{space 2}  14.2304{col 44}{space 1}   -0.81{col 53}{space 3}0.419{col 61}{space 4}-39.38032{col 74}{space 3} 16.40183
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA43, title((A43))
{txt}
{com}. //Model A44
. fracreg logit avgprosanctionsvote loggedmigrants population unemployment gdpgrowth distance col pincometax sstax i.bill i.ccode

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-40.753653}  
Iteration 1:{space 3}log pseudolikelihood = {res:  -32.2824}  
Iteration 2:{space 3}log pseudolikelihood = {res:-32.199832}  
Iteration 3:{space 3}log pseudolikelihood = {res:-32.199569}  
Iteration 4:{space 3}log pseudolikelihood = {res:-32.199569}  
{res}
{txt}Fractional logistic regression{col 49}Number of obs{col 67}= {res}        66
{txt}{col 49}Wald chi2({res}31{txt}){col 67}= {res}    526.99
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-32.199569{txt}{col 49}Pseudo R2{col 67}= {res}    0.2875

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}avgprosanctionsvote{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}loggedmigrants {c |}{col 21}{res}{space 2}-.1573176{col 33}{space 2} .0630945{col 44}{space 1}   -2.49{col 53}{space 3}0.013{col 61}{space 4}-.2809805{col 74}{space 3}-.0336546
{txt}{space 9}population {c |}{col 21}{res}{space 2} .2076315{col 33}{space 2} .2554698{col 44}{space 1}    0.81{col 53}{space 3}0.416{col 61}{space 4}-.2930801{col 74}{space 3} .7083432
{txt}{space 7}unemployment {c |}{col 21}{res}{space 2}-.0311887{col 33}{space 2} .0490404{col 44}{space 1}   -0.64{col 53}{space 3}0.525{col 61}{space 4}-.1273062{col 74}{space 3} .0649287
{txt}{space 10}gdpgrowth {c |}{col 21}{res}{space 2}-.0938127{col 33}{space 2} .1106384{col 44}{space 1}   -0.85{col 53}{space 3}0.396{col 61}{space 4}-.3106601{col 74}{space 3} .1230346
{txt}{space 11}distance {c |}{col 21}{res}{space 2} .0001484{col 33}{space 2} .0002876{col 44}{space 1}    0.52{col 53}{space 3}0.606{col 61}{space 4}-.0004153{col 74}{space 3}  .000712
{txt}{space 16}col {c |}{col 21}{res}{space 2} .0453072{col 33}{space 2} .4733903{col 44}{space 1}    0.10{col 53}{space 3}0.924{col 61}{space 4}-.8825209{col 74}{space 3} .9731352
{txt}{space 9}pincometax {c |}{col 21}{res}{space 2}-.3304431{col 33}{space 2} .2186964{col 44}{space 1}   -1.51{col 53}{space 3}0.131{col 61}{space 4}-.7590802{col 74}{space 3} .0981941
{txt}{space 14}sstax {c |}{col 21}{res}{space 2}-.3087907{col 33}{space 2} .3698888{col 44}{space 1}   -0.83{col 53}{space 3}0.404{col 61}{space 4}-1.033759{col 74}{space 3}  .416178
{txt}{space 19} {c |}
{space 15}bill {c |}
{space 17}2  {c |}{col 21}{res}{space 2} 3.181159{col 33}{space 2} .4292261{col 44}{space 1}    7.41{col 53}{space 3}0.000{col 61}{space 4} 2.339891{col 74}{space 3} 4.022427
{txt}{space 17}3  {c |}{col 21}{res}{space 2} 2.908928{col 33}{space 2} .4379995{col 44}{space 1}    6.64{col 53}{space 3}0.000{col 61}{space 4} 2.050465{col 74}{space 3} 3.767391
{txt}{space 19} {c |}
{space 14}ccode {c |}
{space 15}205  {c |}{col 21}{res}{space 2}  10.3652{col 33}{space 2} 15.18899{col 44}{space 1}    0.68{col 53}{space 3}0.495{col 61}{space 4}-19.40467{col 74}{space 3} 40.13507
{txt}{space 15}210  {c |}{col 21}{res}{space 2} 11.37899{col 33}{space 2} 12.02851{col 44}{space 1}    0.95{col 53}{space 3}0.344{col 61}{space 4}-12.19646{col 74}{space 3} 34.95444
{txt}{space 15}211  {c |}{col 21}{res}{space 2} 13.37928{col 33}{space 2} 13.10518{col 44}{space 1}    1.02{col 53}{space 3}0.307{col 61}{space 4} -12.3064{col 74}{space 3} 39.06496
{txt}{space 15}212  {c |}{col 21}{res}{space 2}  13.2202{col 33}{space 2} 15.84664{col 44}{space 1}    0.83{col 53}{space 3}0.404{col 61}{space 4}-17.83865{col 74}{space 3} 44.27905
{txt}{space 15}220  {c |}{col 21}{res}{space 2} 1.672559{col 33}{space 2} 4.453754{col 44}{space 1}    0.38{col 53}{space 3}0.707{col 61}{space 4}-7.056638{col 74}{space 3} 10.40176
{txt}{space 15}230  {c |}{col 21}{res}{space 2} 3.838662{col 33}{space 2} 4.790879{col 44}{space 1}    0.80{col 53}{space 3}0.423{col 61}{space 4}-5.551287{col 74}{space 3} 13.22861
{txt}{space 15}235  {c |}{col 21}{res}{space 2} 10.23879{col 33}{space 2} 13.51714{col 44}{space 1}    0.76{col 53}{space 3}0.449{col 61}{space 4}-16.25432{col 74}{space 3} 36.73191
{txt}{space 15}255  {c |}{col 21}{res}{space 2}-1.685253{col 33}{space 2} 5.603748{col 44}{space 1}   -0.30{col 53}{space 3}0.764{col 61}{space 4} -12.6684{col 74}{space 3} 9.297892
{txt}{space 15}290  {c |}{col 21}{res}{space 2} 5.331701{col 33}{space 2} 6.974164{col 44}{space 1}    0.76{col 53}{space 3}0.445{col 61}{space 4} -8.33741{col 74}{space 3} 19.00081
{txt}{space 15}305  {c |}{col 21}{res}{space 2} 14.16037{col 33}{space 2} 14.14996{col 44}{space 1}    1.00{col 53}{space 3}0.317{col 61}{space 4}-13.57304{col 74}{space 3} 41.89378
{txt}{space 15}310  {c |}{col 21}{res}{space 2} 11.69444{col 33}{space 2} 13.92341{col 44}{space 1}    0.84{col 53}{space 3}0.401{col 61}{space 4}-15.59493{col 74}{space 3} 38.98381
{txt}{space 15}316  {c |}{col 21}{res}{space 2} 11.53512{col 33}{space 2} 13.80475{col 44}{space 1}    0.84{col 53}{space 3}0.403{col 61}{space 4}-15.52169{col 74}{space 3} 38.59193
{txt}{space 15}317  {c |}{col 21}{res}{space 2} 11.86273{col 33}{space 2} 15.29859{col 44}{space 1}    0.78{col 53}{space 3}0.438{col 61}{space 4}-18.12194{col 74}{space 3} 41.84741
{txt}{space 15}325  {c |}{col 21}{res}{space 2} 3.092567{col 33}{space 2}  2.82085{col 44}{space 1}    1.10{col 53}{space 3}0.273{col 61}{space 4}-2.436198{col 74}{space 3} 8.621332
{txt}{space 15}349  {c |}{col 21}{res}{space 2} 13.39965{col 33}{space 2} 15.93042{col 44}{space 1}    0.84{col 53}{space 3}0.400{col 61}{space 4} -17.8234{col 74}{space 3}  44.6227
{txt}{space 15}350  {c |}{col 21}{res}{space 2} 9.537454{col 33}{space 2} 13.95032{col 44}{space 1}    0.68{col 53}{space 3}0.494{col 61}{space 4}-17.80467{col 74}{space 3} 36.87958
{txt}{space 15}366  {c |}{col 21}{res}{space 2}  11.9736{col 33}{space 2}  15.8436{col 44}{space 1}    0.76{col 53}{space 3}0.450{col 61}{space 4}-19.07927{col 74}{space 3} 43.02648
{txt}{space 15}367  {c |}{col 21}{res}{space 2}  11.4247{col 33}{space 2} 15.89622{col 44}{space 1}    0.72{col 53}{space 3}0.472{col 61}{space 4}-19.73132{col 74}{space 3} 42.58071
{txt}{space 15}375  {c |}{col 21}{res}{space 2} 15.04225{col 33}{space 2}   14.527{col 44}{space 1}    1.04{col 53}{space 3}0.300{col 61}{space 4}-13.43014{col 74}{space 3} 43.51465
{txt}{space 15}380  {c |}{col 21}{res}{space 2} 14.04694{col 33}{space 2} 13.53601{col 44}{space 1}    1.04{col 53}{space 3}0.299{col 61}{space 4}-12.48315{col 74}{space 3} 40.57703
{txt}{space 15}390  {c |}{col 21}{res}{space 2} 15.22876{col 33}{space 2} 14.21491{col 44}{space 1}    1.07{col 53}{space 3}0.284{col 61}{space 4}-12.63196{col 74}{space 3} 43.08948
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-8.288189{col 33}{space 2} 17.60878{col 44}{space 1}   -0.47{col 53}{space 3}0.638{col 61}{space 4}-42.80076{col 74}{space 3} 26.22439
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA44, title((A44))
{txt}
{com}. //Model A45
. fracreg logit avgprosanctionsvote loggedmigrants population unemployment gdpgrowth distance col logged_refugee i.bill i.ccode

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-42.814072}  
Iteration 1:{space 3}log pseudolikelihood = {res:-33.038528}  
Iteration 2:{space 3}log pseudolikelihood = {res:-32.864656}  
Iteration 3:{space 3}log pseudolikelihood = {res:-32.847965}  
Iteration 4:{space 3}log pseudolikelihood = {res:-32.845224}  
Iteration 5:{space 3}log pseudolikelihood = {res:-32.844584}  
Iteration 6:{space 3}log pseudolikelihood = {res:-32.844432}  
Iteration 7:{space 3}log pseudolikelihood = {res:-32.844401}  
Iteration 8:{space 3}log pseudolikelihood = {res:-32.844396}  
Iteration 9:{space 3}log pseudolikelihood = {res:-32.844395}  
{res}
{txt}Fractional logistic regression{col 49}Number of obs{col 67}= {res}        71
{txt}{col 49}Wald chi2({res}34{txt}){col 67}= {res}    676.17
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-32.844395{txt}{col 49}Pseudo R2{col 67}= {res}    0.3193

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}avgprosanctionsvote{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}loggedmigrants {c |}{col 21}{res}{space 2} -.208837{col 33}{space 2} .1205834{col 44}{space 1}   -1.73{col 53}{space 3}0.083{col 61}{space 4} -.445176{col 74}{space 3} .0275021
{txt}{space 9}population {c |}{col 21}{res}{space 2}  .190262{col 33}{space 2} .2868651{col 44}{space 1}    0.66{col 53}{space 3}0.507{col 61}{space 4}-.3719834{col 74}{space 3} .7525073
{txt}{space 7}unemployment {c |}{col 21}{res}{space 2}-.1225466{col 33}{space 2} .0652159{col 44}{space 1}   -1.88{col 53}{space 3}0.060{col 61}{space 4}-.2503673{col 74}{space 3} .0052741
{txt}{space 10}gdpgrowth {c |}{col 21}{res}{space 2} .1053008{col 33}{space 2} .1066602{col 44}{space 1}    0.99{col 53}{space 3}0.324{col 61}{space 4}-.1037493{col 74}{space 3}  .314351
{txt}{space 11}distance {c |}{col 21}{res}{space 2}  .000221{col 33}{space 2}  .000351{col 44}{space 1}    0.63{col 53}{space 3}0.529{col 61}{space 4}-.0004669{col 74}{space 3} .0009089
{txt}{space 16}col {c |}{col 21}{res}{space 2}  .624975{col 33}{space 2} .4644116{col 44}{space 1}    1.35{col 53}{space 3}0.178{col 61}{space 4} -.285255{col 74}{space 3} 1.535205
{txt}{space 4}logged_refugees {c |}{col 21}{res}{space 2} .0657512{col 33}{space 2} .1593479{col 44}{space 1}    0.41{col 53}{space 3}0.680{col 61}{space 4}-.2465649{col 74}{space 3} .3780673
{txt}{space 19} {c |}
{space 15}bill {c |}
{space 17}2  {c |}{col 21}{res}{space 2} 3.391953{col 33}{space 2} .5412935{col 44}{space 1}    6.27{col 53}{space 3}0.000{col 61}{space 4} 2.331037{col 74}{space 3} 4.452868
{txt}{space 17}3  {c |}{col 21}{res}{space 2} 2.920516{col 33}{space 2} .4700909{col 44}{space 1}    6.21{col 53}{space 3}0.000{col 61}{space 4} 1.999155{col 74}{space 3} 3.841877
{txt}{space 19} {c |}
{space 14}ccode {c |}
{space 15}205  {c |}{col 21}{res}{space 2} 9.891797{col 33}{space 2} 16.98539{col 44}{space 1}    0.58{col 53}{space 3}0.560{col 61}{space 4}-23.39895{col 74}{space 3} 43.18255
{txt}{space 15}210  {c |}{col 21}{res}{space 2} 8.843525{col 33}{space 2} 13.42339{col 44}{space 1}    0.66{col 53}{space 3}0.510{col 61}{space 4}-17.46584{col 74}{space 3} 35.15289
{txt}{space 15}211  {c |}{col 21}{res}{space 2} 9.025551{col 33}{space 2} 14.96497{col 44}{space 1}    0.60{col 53}{space 3}0.546{col 61}{space 4}-20.30524{col 74}{space 3} 38.35634
{txt}{space 15}212  {c |}{col 21}{res}{space 2} 10.06798{col 33}{space 2}  18.0187{col 44}{space 1}    0.56{col 53}{space 3}0.576{col 61}{space 4}-25.24803{col 74}{space 3} 45.38398
{txt}{space 15}220  {c |}{col 21}{res}{space 2}-1.295107{col 33}{space 2} .6892839{col 44}{space 1}   -1.88{col 53}{space 3}0.060{col 61}{space 4}-2.646078{col 74}{space 3}  .055865
{txt}{space 15}230  {c |}{col 21}{res}{space 2} 4.456129{col 33}{space 2} 5.272849{col 44}{space 1}    0.85{col 53}{space 3}0.398{col 61}{space 4}-5.878466{col 74}{space 3} 14.79072
{txt}{space 15}235  {c |}{col 21}{res}{space 2} 10.21843{col 33}{space 2}  15.2828{col 44}{space 1}    0.67{col 53}{space 3}0.504{col 61}{space 4}-19.73531{col 74}{space 3} 40.17217
{txt}{space 15}255  {c |}{col 21}{res}{space 2}-3.933932{col 33}{space 2} 4.958429{col 44}{space 1}   -0.79{col 53}{space 3}0.428{col 61}{space 4}-13.65228{col 74}{space 3} 5.784411
{txt}{space 15}290  {c |}{col 21}{res}{space 2} 4.875903{col 33}{space 2} 7.620065{col 44}{space 1}    0.64{col 53}{space 3}0.522{col 61}{space 4}-10.05915{col 74}{space 3} 19.81095
{txt}{space 15}305  {c |}{col 21}{res}{space 2} 9.600504{col 33}{space 2} 16.00772{col 44}{space 1}    0.60{col 53}{space 3}0.549{col 61}{space 4}-21.77406{col 74}{space 3} 40.97506
{txt}{space 15}310  {c |}{col 21}{res}{space 2} 10.21165{col 33}{space 2} 15.73017{col 44}{space 1}    0.65{col 53}{space 3}0.516{col 61}{space 4}-20.61891{col 74}{space 3} 41.04221
{txt}{space 15}316  {c |}{col 21}{res}{space 2} 10.10446{col 33}{space 2} 15.31911{col 44}{space 1}    0.66{col 53}{space 3}0.510{col 61}{space 4}-19.92044{col 74}{space 3} 40.12936
{txt}{space 15}317  {c |}{col 21}{res}{space 2} 11.32152{col 33}{space 2} 17.04239{col 44}{space 1}    0.66{col 53}{space 3}0.506{col 61}{space 4}-22.08094{col 74}{space 3} 44.72398
{txt}{space 15}325  {c |}{col 21}{res}{space 2} .8542716{col 33}{space 2} 1.414145{col 44}{space 1}    0.60{col 53}{space 3}0.546{col 61}{space 4}-1.917401{col 74}{space 3} 3.625944
{txt}{space 15}338  {c |}{col 21}{res}{space 2} 11.82563{col 33}{space 2} 18.40094{col 44}{space 1}    0.64{col 53}{space 3}0.520{col 61}{space 4}-24.23956{col 74}{space 3} 47.89081
{txt}{space 15}349  {c |}{col 21}{res}{space 2} 25.72634{col 33}{space 2} 17.65981{col 44}{space 1}    1.46{col 53}{space 3}0.145{col 61}{space 4}-8.886259{col 74}{space 3} 60.33894
{txt}{space 15}350  {c |}{col 21}{res}{space 2} 10.82239{col 33}{space 2} 15.68868{col 44}{space 1}    0.69{col 53}{space 3}0.490{col 61}{space 4}-19.92685{col 74}{space 3} 41.57163
{txt}{space 15}352  {c |}{col 21}{res}{space 2} 12.56882{col 33}{space 2} 18.32342{col 44}{space 1}    0.69{col 53}{space 3}0.493{col 61}{space 4}-23.34443{col 74}{space 3} 48.48207
{txt}{space 15}355  {c |}{col 21}{res}{space 2} 11.20873{col 33}{space 2} 16.47224{col 44}{space 1}    0.68{col 53}{space 3}0.496{col 61}{space 4}-21.07626{col 74}{space 3} 43.49373
{txt}{space 15}360  {c |}{col 21}{res}{space 2} 8.860407{col 33}{space 2} 12.79769{col 44}{space 1}    0.69{col 53}{space 3}0.489{col 61}{space 4} -16.2226{col 74}{space 3} 33.94342
{txt}{space 15}367  {c |}{col 21}{res}{space 2} 10.26045{col 33}{space 2} 17.83266{col 44}{space 1}    0.58{col 53}{space 3}0.565{col 61}{space 4}-24.69093{col 74}{space 3} 45.21182
{txt}{space 15}368  {c |}{col 21}{res}{space 2} 10.60503{col 33}{space 2}  17.5867{col 44}{space 1}    0.60{col 53}{space 3}0.546{col 61}{space 4}-23.86427{col 74}{space 3} 45.07433
{txt}{space 15}375  {c |}{col 21}{res}{space 2} 11.30403{col 33}{space 2} 16.66962{col 44}{space 1}    0.68{col 53}{space 3}0.498{col 61}{space 4}-21.36782{col 74}{space 3} 43.97589
{txt}{space 15}380  {c |}{col 21}{res}{space 2} 9.500588{col 33}{space 2} 15.46441{col 44}{space 1}    0.61{col 53}{space 3}0.539{col 61}{space 4}-20.80911{col 74}{space 3} 39.81028
{txt}{space 15}390  {c |}{col 21}{res}{space 2} 10.50929{col 33}{space 2}   16.629{col 44}{space 1}    0.63{col 53}{space 3}0.527{col 61}{space 4}-22.08294{col 74}{space 3} 43.10153
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-12.11747{col 33}{space 2} 18.23333{col 44}{space 1}   -0.66{col 53}{space 3}0.506{col 61}{space 4}-47.85415{col 74}{space 3} 23.61921
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA45, title((A45))
{txt}
{com}. //Model A46
. fracreg logit avgprosanctionsvote loggedmigrants population unemployment gdpgrowth distance col lnremit i.bill i.ccode

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-42.742225}  
Iteration 1:{space 3}log pseudolikelihood = {res:-31.365825}  
Iteration 2:{space 3}log pseudolikelihood = {res:-31.217768}  
Iteration 3:{space 3}log pseudolikelihood = {res:-31.216663}  
Iteration 4:{space 3}log pseudolikelihood = {res:-31.216663}  
{res}
{txt}Fractional logistic regression{col 49}Number of obs{col 67}= {res}        69
{txt}{col 49}Wald chi2({res}35{txt}){col 67}= {res}    988.11
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-31.216663{txt}{col 49}Pseudo R2{col 67}= {res}    0.3458

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}avgprosanctionsvote{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}loggedmigrants {c |}{col 21}{res}{space 2}-.4094355{col 33}{space 2} .0779924{col 44}{space 1}   -5.25{col 53}{space 3}0.000{col 61}{space 4}-.5622979{col 74}{space 3}-.2565732
{txt}{space 9}population {c |}{col 21}{res}{space 2} .9941612{col 33}{space 2} .2930987{col 44}{space 1}    3.39{col 53}{space 3}0.001{col 61}{space 4} .4196982{col 74}{space 3} 1.568624
{txt}{space 7}unemployment {c |}{col 21}{res}{space 2}-.1441613{col 33}{space 2} .0656209{col 44}{space 1}   -2.20{col 53}{space 3}0.028{col 61}{space 4}-.2727759{col 74}{space 3}-.0155467
{txt}{space 10}gdpgrowth {c |}{col 21}{res}{space 2} .0224228{col 33}{space 2} .1159737{col 44}{space 1}    0.19{col 53}{space 3}0.847{col 61}{space 4}-.2048815{col 74}{space 3} .2497271
{txt}{space 11}distance {c |}{col 21}{res}{space 2} .0009079{col 33}{space 2} .0003747{col 44}{space 1}    2.42{col 53}{space 3}0.015{col 61}{space 4} .0001735{col 74}{space 3} .0016423
{txt}{space 16}col {c |}{col 21}{res}{space 2}  1.55948{col 33}{space 2} .4919239{col 44}{space 1}    3.17{col 53}{space 3}0.002{col 61}{space 4} .5953273{col 74}{space 3} 2.523634
{txt}{space 12}lnremit {c |}{col 21}{res}{space 2}-.2948755{col 33}{space 2}  .218556{col 44}{space 1}   -1.35{col 53}{space 3}0.177{col 61}{space 4}-.7232373{col 74}{space 3} .1334864
{txt}{space 19} {c |}
{space 15}bill {c |}
{space 17}2  {c |}{col 21}{res}{space 2} 2.791513{col 33}{space 2} .4348274{col 44}{space 1}    6.42{col 53}{space 3}0.000{col 61}{space 4} 1.939267{col 74}{space 3} 3.643759
{txt}{space 17}3  {c |}{col 21}{res}{space 2} 1.985892{col 33}{space 2} .4376459{col 44}{space 1}    4.54{col 53}{space 3}0.000{col 61}{space 4} 1.128122{col 74}{space 3} 2.843662
{txt}{space 19} {c |}
{space 14}ccode {c |}
{space 15}205  {c |}{col 21}{res}{space 2} 55.50885{col 33}{space 2} 16.92735{col 44}{space 1}    3.28{col 53}{space 3}0.001{col 61}{space 4} 22.33185{col 74}{space 3} 88.68585
{txt}{space 15}210  {c |}{col 21}{res}{space 2} 46.40168{col 33}{space 2} 13.65666{col 44}{space 1}    3.40{col 53}{space 3}0.001{col 61}{space 4} 19.63512{col 74}{space 3} 73.16825
{txt}{space 15}211  {c |}{col 21}{res}{space 2} 50.26454{col 33}{space 2} 15.12092{col 44}{space 1}    3.32{col 53}{space 3}0.001{col 61}{space 4} 20.62808{col 74}{space 3} 79.90099
{txt}{space 15}212  {c |}{col 21}{res}{space 2}  58.4831{col 33}{space 2} 17.96084{col 44}{space 1}    3.26{col 53}{space 3}0.001{col 61}{space 4}  23.2805{col 74}{space 3}  93.6857
{txt}{space 15}220  {c |}{col 21}{res}{space 2}-2.848276{col 33}{space 2} .7006004{col 44}{space 1}   -4.07{col 53}{space 3}0.000{col 61}{space 4}-4.221427{col 74}{space 3}-1.475124
{txt}{space 15}230  {c |}{col 21}{res}{space 2} 17.20036{col 33}{space 2}  5.27634{col 44}{space 1}    3.26{col 53}{space 3}0.001{col 61}{space 4} 6.858924{col 74}{space 3}  27.5418
{txt}{space 15}235  {c |}{col 21}{res}{space 2} 49.41991{col 33}{space 2} 15.00846{col 44}{space 1}    3.29{col 53}{space 3}0.001{col 61}{space 4} 20.00387{col 74}{space 3} 78.83594
{txt}{space 15}255  {c |}{col 21}{res}{space 2}-16.86469{col 33}{space 2} 4.887484{col 44}{space 1}   -3.45{col 53}{space 3}0.001{col 61}{space 4}-26.44398{col 74}{space 3}-7.285394
{txt}{space 15}290  {c |}{col 21}{res}{space 2} 24.65536{col 33}{space 2} 7.430883{col 44}{space 1}    3.32{col 53}{space 3}0.001{col 61}{space 4}  10.0911{col 74}{space 3} 39.21963
{txt}{space 15}305  {c |}{col 21}{res}{space 2} 53.99238{col 33}{space 2} 16.11164{col 44}{space 1}    3.35{col 53}{space 3}0.001{col 61}{space 4} 22.41415{col 74}{space 3}  85.5706
{txt}{space 15}310  {c |}{col 21}{res}{space 2} 53.18183{col 33}{space 2} 15.74511{col 44}{space 1}    3.38{col 53}{space 3}0.001{col 61}{space 4} 22.32199{col 74}{space 3} 84.04168
{txt}{space 15}316  {c |}{col 21}{res}{space 2} 51.43044{col 33}{space 2} 15.22438{col 44}{space 1}    3.38{col 53}{space 3}0.001{col 61}{space 4} 21.59121{col 74}{space 3} 81.26968
{txt}{space 15}317  {c |}{col 21}{res}{space 2} 56.51517{col 33}{space 2} 16.96079{col 44}{space 1}    3.33{col 53}{space 3}0.001{col 61}{space 4} 23.27264{col 74}{space 3} 89.75771
{txt}{space 15}325  {c |}{col 21}{res}{space 2}  4.12476{col 33}{space 2}  1.30555{col 44}{space 1}    3.16{col 53}{space 3}0.002{col 61}{space 4} 1.565929{col 74}{space 3} 6.683591
{txt}{space 15}338  {c |}{col 21}{res}{space 2} 60.10622{col 33}{space 2} 18.13857{col 44}{space 1}    3.31{col 53}{space 3}0.001{col 61}{space 4} 24.55528{col 74}{space 3} 95.65716
{txt}{space 15}349  {c |}{col 21}{res}{space 2} 58.61099{col 33}{space 2} 17.51611{col 44}{space 1}    3.35{col 53}{space 3}0.001{col 61}{space 4} 24.28004{col 74}{space 3} 92.94194
{txt}{space 15}350  {c |}{col 21}{res}{space 2} 53.59245{col 33}{space 2} 15.77838{col 44}{space 1}    3.40{col 53}{space 3}0.001{col 61}{space 4}  22.6674{col 74}{space 3}  84.5175
{txt}{space 15}352  {c |}{col 21}{res}{space 2} 63.31027{col 33}{space 2} 18.31298{col 44}{space 1}    3.46{col 53}{space 3}0.001{col 61}{space 4} 27.41749{col 74}{space 3} 99.20304
{txt}{space 15}355  {c |}{col 21}{res}{space 2} 54.96761{col 33}{space 2} 16.34885{col 44}{space 1}    3.36{col 53}{space 3}0.001{col 61}{space 4} 22.92446{col 74}{space 3} 87.01076
{txt}{space 15}360  {c |}{col 21}{res}{space 2} 43.83732{col 33}{space 2} 12.73067{col 44}{space 1}    3.44{col 53}{space 3}0.001{col 61}{space 4} 18.88567{col 74}{space 3} 68.78898
{txt}{space 15}366  {c |}{col 21}{res}{space 2} 58.03471{col 33}{space 2} 17.68841{col 44}{space 1}    3.28{col 53}{space 3}0.001{col 61}{space 4} 23.36606{col 74}{space 3} 92.70337
{txt}{space 15}367  {c |}{col 21}{res}{space 2} 59.14654{col 33}{space 2} 17.70618{col 44}{space 1}    3.34{col 53}{space 3}0.001{col 61}{space 4} 24.44306{col 74}{space 3} 93.85002
{txt}{space 15}368  {c |}{col 21}{res}{space 2} 57.52448{col 33}{space 2} 17.47941{col 44}{space 1}    3.29{col 53}{space 3}0.001{col 61}{space 4} 23.26546{col 74}{space 3} 91.78351
{txt}{space 15}375  {c |}{col 21}{res}{space 2} 56.91158{col 33}{space 2} 16.77062{col 44}{space 1}    3.39{col 53}{space 3}0.001{col 61}{space 4} 24.04177{col 74}{space 3}  89.7814
{txt}{space 15}380  {c |}{col 21}{res}{space 2} 53.48332{col 33}{space 2} 15.81905{col 44}{space 1}    3.38{col 53}{space 3}0.001{col 61}{space 4} 22.47856{col 74}{space 3} 84.48809
{txt}{space 15}390  {c |}{col 21}{res}{space 2} 56.69109{col 33}{space 2} 16.84045{col 44}{space 1}    3.37{col 53}{space 3}0.001{col 61}{space 4} 23.68442{col 74}{space 3} 89.69776
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-61.75171{col 33}{space 2} 17.95385{col 44}{space 1}   -3.44{col 53}{space 3}0.001{col 61}{space 4}-96.94062{col 74}{space 3} -26.5628
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA46, title((A46))
{txt}
{com}. 
. estout MODELA37 MODELA38 MODELA39 MODELA40 MODELA41 MODELA42 MODELA43 MODELA44 MODELA45 MODELA46, cells(b(star fmt(3)) se(par fmt(3))) starlevels($^{c -(}+{c )-}$ 0.10 $^{c -(}*{c )-}$ 0.05 $^{c -(}**{c )-}$ 0.01 $^{c -(}***{c )-}$ 0.001) stats(N N_g r2, fmt(0 0 3) label(Observations Countries R$^2$)) label,  using "modelsa37_a46.tex", replace style(tex)
{res}{txt}(note: file modelsa37_a46.tex not found)
(output written to {browse  `"modelsa37_a46.tex"'})

{com}. 
. *Table A7 (Mixed Effects)
. //Re-upload uncollapsed data
. clear
{txt}
{com}. use "C:\Users\Brendan\Desktop\Replication File_FPA\Main Data.dta"
{txt}
{com}. 
. //Model A47
. melogit prosanctions loggedmigrants libyabill iranbill i.party || ccode:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-667.92124}  
Iteration 1:{space 3}log likelihood = {res:-652.72872}  
Iteration 2:{space 3}log likelihood = {res:-652.12185}  
Iteration 3:{space 3}log likelihood = {res:-652.11937}  
Iteration 4:{space 3}log likelihood = {res:-652.11937}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-662.05079}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-662.05079}  (not concave)
Iteration 1:{space 3}log likelihood = {res:-653.34735}  (not concave)
Iteration 2:{space 3}log likelihood = {res:-650.70219}  
Iteration 3:{space 3}log likelihood = {res:-650.56245}  
Iteration 4:{space 3}log likelihood = {res:-650.56229}  
Iteration 5:{space 3}log likelihood = {res:-650.56229}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     1,713
{txt}Group variable: {col 27}{res}ccode{col 49}{txt}Number of groups{col 67}={res}{col 69}        28

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        14
{col 63}{txt}avg{col 67}={res}{col 69}      61.2
{col 63}{txt}max{col 67}={res}{col 69}       235

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}10{txt}){col 67}={res}{col 70}   570.45
{txt}Log likelihood = {res}-650.56229{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  prosanctions{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
loggedmigrants {c |}{col 16}{res}{space 2}-.0744205{col 28}{space 2} .0312486{col 39}{space 1}   -2.38{col 48}{space 3}0.017{col 56}{space 4}-.1356665{col 69}{space 3}-.0131744
{txt}{space 5}libyabill {c |}{col 16}{res}{space 2}  3.67187{col 28}{space 2}  .197349{col 39}{space 1}   18.61{col 48}{space 3}0.000{col 56}{space 4} 3.285073{col 69}{space 3} 4.058667
{txt}{space 6}iranbill {c |}{col 16}{res}{space 2} 3.866351{col 28}{space 2} .1970115{col 39}{space 1}   19.62{col 48}{space 3}0.000{col 56}{space 4} 3.480216{col 69}{space 3} 4.252487
{txt}{space 14} {c |}
{space 9}party {c |}
{space 10}EPP  {c |}{col 16}{res}{space 2} 2.230623{col 28}{space 2} .3014422{col 39}{space 1}    7.40{col 48}{space 3}0.000{col 56}{space 4} 1.639807{col 69}{space 3} 2.821438
{txt}{space 10}S&D  {c |}{col 16}{res}{space 2} 2.226302{col 28}{space 2} .3064748{col 39}{space 1}    7.26{col 48}{space 3}0.000{col 56}{space 4} 1.625622{col 69}{space 3} 2.826981
{txt}{space 3}Greens/EFA  {c |}{col 16}{res}{space 2}-.6768214{col 28}{space 2} .3527672{col 39}{space 1}   -1.92{col 48}{space 3}0.055{col 56}{space 4}-1.368232{col 69}{space 3} .0145896
{txt}{space 4}ALDE/ADLE  {c |}{col 16}{res}{space 2} 2.398712{col 28}{space 2} .3563596{col 39}{space 1}    6.73{col 48}{space 3}0.000{col 56}{space 4}  1.70026{col 69}{space 3} 3.097164
{txt}{space 10}ECR  {c |}{col 16}{res}{space 2} 4.251344{col 28}{space 2} .7959306{col 39}{space 1}    5.34{col 48}{space 3}0.000{col 56}{space 4} 2.691348{col 69}{space 3} 5.811339
{txt}{space 9}EFDD  {c |}{col 16}{res}{space 2} .1652268{col 28}{space 2} .4378689{col 39}{space 1}    0.38{col 48}{space 3}0.706{col 56}{space 4}-.6929804{col 69}{space 3} 1.023434
{txt}{space 6}GUE-NGL  {c |}{col 16}{res}{space 2} -.894662{col 28}{space 2}  .378018{col 39}{space 1}   -2.37{col 48}{space 3}0.018{col 56}{space 4}-1.635564{col 69}{space 3}-.1537603
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2}-3.367549{col 28}{space 2} .4040978{col 39}{space 1}   -8.33{col 48}{space 3}0.000{col 56}{space 4}-4.159566{col 69}{space 3}-2.575532
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}ccode         {col 16}{txt}{c |}
{space 5}var(_cons){c |}{col 16}{res}{space 2} .0660744{col 28}{space 2} .0564557{col 56}{space 4} .0123806{col 69}{space 3} .3526329
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 3.11{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0388
{txt}
{com}. estimates store MODELA47, title((A47))
{txt}
{com}. //Model A48
. melogit prosanctions loggedmigrants population unemployment gdpgrowth distance col libyabill iranbill  || ccode:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-814.12322}  
Iteration 1:{space 3}log likelihood = {res:-811.86769}  
Iteration 2:{space 3}log likelihood = {res:-811.86277}  
Iteration 3:{space 3}log likelihood = {res:-811.86277}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-817.76767}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-817.76767}  (not concave)
Iteration 1:{space 3}log likelihood = {res:-806.99605}  
Iteration 2:{space 3}log likelihood = {res:-806.79183}  
Iteration 3:{space 3}log likelihood = {res:-806.79057}  
Iteration 4:{space 3}log likelihood = {res:-806.79057}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     1,713
{txt}Group variable: {col 27}{res}ccode{col 49}{txt}Number of groups{col 67}={res}{col 69}        28

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        14
{col 63}{txt}avg{col 67}={res}{col 69}      61.2
{col 63}{txt}max{col 67}={res}{col 69}       235

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}   472.15
{txt}Log likelihood = {res}-806.79057{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  prosanctions{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
loggedmigrants {c |}{col 16}{res}{space 2}-.1468205{col 28}{space 2} .0395524{col 39}{space 1}   -3.71{col 48}{space 3}0.000{col 56}{space 4}-.2243418{col 69}{space 3}-.0692991
{txt}{space 4}population {c |}{col 16}{res}{space 2} .0013238{col 28}{space 2} .0041636{col 39}{space 1}    0.32{col 48}{space 3}0.751{col 56}{space 4}-.0068367{col 69}{space 3} .0094842
{txt}{space 2}unemployment {c |}{col 16}{res}{space 2}-.0588697{col 28}{space 2} .0195143{col 39}{space 1}   -3.02{col 48}{space 3}0.003{col 56}{space 4} -.097117{col 69}{space 3}-.0206224
{txt}{space 5}gdpgrowth {c |}{col 16}{res}{space 2} .0454917{col 28}{space 2} .0514759{col 39}{space 1}    0.88{col 48}{space 3}0.377{col 56}{space 4}-.0553991{col 69}{space 3} .1463826
{txt}{space 6}distance {c |}{col 16}{res}{space 2} .0001648{col 28}{space 2} .0001286{col 39}{space 1}    1.28{col 48}{space 3}0.200{col 56}{space 4}-.0000873{col 69}{space 3} .0004169
{txt}{space 11}col {c |}{col 16}{res}{space 2} .3708187{col 28}{space 2} .3122235{col 39}{space 1}    1.19{col 48}{space 3}0.235{col 56}{space 4}-.2411281{col 69}{space 3} .9827655
{txt}{space 5}libyabill {c |}{col 16}{res}{space 2} 3.025351{col 28}{space 2} .1977154{col 39}{space 1}   15.30{col 48}{space 3}0.000{col 56}{space 4} 2.637836{col 69}{space 3} 3.412866
{txt}{space 6}iranbill {c |}{col 16}{res}{space 2} 3.340005{col 28}{space 2}  .239917{col 39}{space 1}   13.92{col 48}{space 3}0.000{col 56}{space 4} 2.869777{col 69}{space 3} 3.810234
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.7789803{col 28}{space 2} .5308858{col 39}{space 1}   -1.47{col 48}{space 3}0.142{col 56}{space 4}-1.819497{col 69}{space 3} .2615368
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}ccode         {col 16}{txt}{c |}
{space 5}var(_cons){c |}{col 16}{res}{space 2}   .09419{col 28}{space 2} .0567009{col 56}{space 4} .0289464{col 69}{space 3}  .306489
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 10.14{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0007
{txt}
{com}. estimates store MODELA48, title((A48))
{txt}
{com}. //Model A49
. melogit prosanctions loggedmigrants population unemployment gdpgrowth distance col immigrantpolicy libyabill iranbill i.party || ccode:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res: -660.9029}  
Iteration 1:{space 3}log likelihood = {res:-644.72197}  
Iteration 2:{space 3}log likelihood = {res:-644.07388}  
Iteration 3:{space 3}log likelihood = {res:-644.07066}  
Iteration 4:{space 3}log likelihood = {res:-644.07066}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-658.23359}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-658.23359}  (not concave)
Iteration 1:{space 3}log likelihood = {res:-649.21299}  (not concave)
Iteration 2:{space 3}log likelihood = {res:-645.60668}  (not concave)
Iteration 3:{space 3}log likelihood = {res:-644.27813}  
Iteration 4:{space 3}log likelihood = {res:-643.96629}  
Iteration 5:{space 3}log likelihood = {res:-643.93491}  
Iteration 6:{space 3}log likelihood = {res: -643.9349}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     1,713
{txt}Group variable: {col 27}{res}ccode{col 49}{txt}Number of groups{col 67}={res}{col 69}        28

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        14
{col 63}{txt}avg{col 67}={res}{col 69}      61.2
{col 63}{txt}max{col 67}={res}{col 69}       235

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}16{txt}){col 67}={res}{col 70}   573.45
{txt}Log likelihood = {res} -643.9349{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   prosanctions{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}loggedmigrants {c |}{col 17}{res}{space 2}-.1124691{col 29}{space 2} .0429538{col 40}{space 1}   -2.62{col 49}{space 3}0.009{col 57}{space 4} -.196657{col 70}{space 3}-.0282812
{txt}{space 5}population {c |}{col 17}{res}{space 2}-.0003446{col 29}{space 2} .0036435{col 40}{space 1}   -0.09{col 49}{space 3}0.925{col 57}{space 4}-.0074856{col 70}{space 3} .0067965
{txt}{space 3}unemployment {c |}{col 17}{res}{space 2}-.0532042{col 29}{space 2} .0189287{col 40}{space 1}   -2.81{col 49}{space 3}0.005{col 57}{space 4}-.0903038{col 70}{space 3}-.0161045
{txt}{space 6}gdpgrowth {c |}{col 17}{res}{space 2}  .011911{col 29}{space 2} .0526877{col 40}{space 1}    0.23{col 49}{space 3}0.821{col 57}{space 4} -.091355{col 70}{space 3}  .115177
{txt}{space 7}distance {c |}{col 17}{res}{space 2} .0002286{col 29}{space 2} .0001258{col 40}{space 1}    1.82{col 49}{space 3}0.069{col 57}{space 4} -.000018{col 70}{space 3} .0004751
{txt}{space 12}col {c |}{col 17}{res}{space 2} .2543659{col 29}{space 2} .3353713{col 40}{space 1}    0.76{col 49}{space 3}0.448{col 57}{space 4}-.4029498{col 70}{space 3} .9116816
{txt}immigrantpolicy {c |}{col 17}{res}{space 2} .0981106{col 29}{space 2} .4285532{col 40}{space 1}    0.23{col 49}{space 3}0.819{col 57}{space 4}-.7418381{col 70}{space 3} .9380594
{txt}{space 6}libyabill {c |}{col 17}{res}{space 2} 3.788895{col 29}{space 2} .2282266{col 40}{space 1}   16.60{col 49}{space 3}0.000{col 57}{space 4}  3.34158{col 70}{space 3} 4.236211
{txt}{space 7}iranbill {c |}{col 17}{res}{space 2} 3.745248{col 29}{space 2} .2591933{col 40}{space 1}   14.45{col 49}{space 3}0.000{col 57}{space 4} 3.237239{col 70}{space 3} 4.253258
{txt}{space 15} {c |}
{space 10}party {c |}
{space 11}EPP  {c |}{col 17}{res}{space 2} 2.225608{col 29}{space 2} .3037515{col 40}{space 1}    7.33{col 49}{space 3}0.000{col 57}{space 4} 1.630266{col 70}{space 3}  2.82095
{txt}{space 11}S&D  {c |}{col 17}{res}{space 2} 2.248098{col 29}{space 2} .3088594{col 40}{space 1}    7.28{col 49}{space 3}0.000{col 57}{space 4} 1.642745{col 70}{space 3} 2.853452
{txt}{space 4}Greens/EFA  {c |}{col 17}{res}{space 2}-.7842183{col 29}{space 2} .3542639{col 40}{space 1}   -2.21{col 49}{space 3}0.027{col 57}{space 4}-1.478563{col 70}{space 3}-.0898739
{txt}{space 5}ALDE/ADLE  {c |}{col 17}{res}{space 2}  2.34383{col 29}{space 2} .3584028{col 40}{space 1}    6.54{col 49}{space 3}0.000{col 57}{space 4} 1.641373{col 70}{space 3} 3.046286
{txt}{space 11}ECR  {c |}{col 17}{res}{space 2} 4.157445{col 29}{space 2} .7986659{col 40}{space 1}    5.21{col 49}{space 3}0.000{col 57}{space 4} 2.592088{col 70}{space 3} 5.722801
{txt}{space 10}EFDD  {c |}{col 17}{res}{space 2} .2430598{col 29}{space 2} .4538283{col 40}{space 1}    0.54{col 49}{space 3}0.592{col 57}{space 4}-.6464273{col 70}{space 3} 1.132547
{txt}{space 7}GUE-NGL  {c |}{col 17}{res}{space 2}-.8059155{col 29}{space 2} .3839692{col 40}{space 1}   -2.10{col 49}{space 3}0.036{col 57}{space 4}-1.558481{col 70}{space 3}-.0533496
{txt}{space 15} {c |}
{space 10}_cons {c |}{col 17}{res}{space 2}-3.435427{col 29}{space 2} 1.177725{col 40}{space 1}   -2.92{col 49}{space 3}0.004{col 57}{space 4}-5.743726{col 70}{space 3}-1.127129
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}ccode          {col 17}{txt}{c |}
{space 6}var(_cons){c |}{col 17}{res}{space 2}  .016521{col 29}{space 2} .0368487{col 57}{space 4} .0002087{col 70}{space 3} 1.307902
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 0.27{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.3012
{txt}
{com}. estimates store MODELA49, title((A49))
{txt}
{com}. //Model A50
. melogit prosanctions loggedmigrants population unemployment gdpgrowth distance col RWPvoteshare libyabill iranbill i.party || ccode:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-660.13888}  
Iteration 1:{space 3}log likelihood = {res:-643.79575}  
Iteration 2:{space 3}log likelihood = {res:-643.14652}  
Iteration 3:{space 3}log likelihood = {res:-643.14293}  
Iteration 4:{space 3}log likelihood = {res:-643.14293}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-657.77245}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-657.77245}  (not concave)
Iteration 1:{space 3}log likelihood = {res:-648.64206}  (not concave)
Iteration 2:{space 3}log likelihood = {res:-644.98276}  (not concave)
Iteration 3:{space 3}log likelihood = {res:-643.59175}  
Iteration 4:{space 3}log likelihood = {res:-643.20434}  
Iteration 5:{space 3}log likelihood = {res:-643.17129}  
Iteration 6:{space 3}log likelihood = {res:-643.16888}  
Iteration 7:{space 3}log likelihood = {res:-643.16774}  (backed up)
Iteration 8:{space 3}log likelihood = {res:-643.16746}  (backed up)
Iteration 9:{space 3}log likelihood = {res:-643.16733}  (backed up)
Iteration 10:{space 2}log likelihood = {res:-643.16733}  (backed up)
Iteration 11:{space 2}log likelihood = {res:-643.16733}  (backed up)
Iteration 12:{space 2}log likelihood = {res:-643.16733}  (backed up)
Iteration 13:{space 2}log likelihood = {res:-643.16733}  (backed up)
Iteration 14:{space 2}log likelihood = {res:-643.16733}  (backed up)
Iteration 15:{space 2}log likelihood = {res:-643.16733}  (backed up)
Iteration 16:{space 2}log likelihood = {res:-643.16733}  (backed up)
Iteration 17:{space 2}log likelihood = {res:-643.16733}  (backed up)
Iteration 18:{space 2}log likelihood = {res:-643.16733}  (backed up)
Iteration 19:{space 2}log likelihood = {res:-643.16733}  (backed up)
Iteration 20:{space 2}log likelihood = {res:-643.16733}  (not concave)
Iteration 21:{space 2}log likelihood = {res:-643.16733}  (backed up)
Iteration 22:{space 2}log likelihood = {res:-643.16733}  (backed up)
Iteration 23:{space 2}log likelihood = {res:-643.16733}  (not concave)
Iteration 24:{space 2}log likelihood = {res:-643.16733}  (backed up)
Iteration 25:{space 2}log likelihood = {res:-643.16733}  (backed up)
Iteration 26:{space 2}log likelihood = {res:-643.16733}  (backed up)
Iteration 27:{space 2}log likelihood = {res:-643.16733}  (not concave)
Iteration 28:{space 2}log likelihood = {res:-643.16733}  (not concave)
Iteration 29:{space 2}log likelihood = {res:-643.16733}  (not concave)
Iteration 30:{space 2}log likelihood = {res:-643.16733}  (not concave)
Iteration 31:{space 2}log likelihood = {res:-643.16733}  (not concave)
Iteration 32:{space 2}log likelihood = {res:-643.16733}  (backed up)
Iteration 33:{space 2}log likelihood = {res:-643.16732}  (not concave)
Iteration 34:{space 2}log likelihood = {res:-643.16732}  (backed up)
Iteration 35:{space 2}log likelihood = {res:-643.16732}  (not concave)
Iteration 36:{space 2}log likelihood = {res:-643.16732}  (not concave)
Iteration 37:{space 2}log likelihood = {res:-643.16732}  
Iteration 38:{space 2}log likelihood = {res:-643.16732}  (not concave)
Iteration 39:{space 2}log likelihood = {res:-643.16732}  
Iteration 40:{space 2}log likelihood = {res:-643.16722}  (not concave)
Iteration 41:{space 2}log likelihood = {res:-643.16722}  
Iteration 42:{space 2}log likelihood = {res:-643.16685}  (backed up)
Iteration 43:{space 2}log likelihood = {res:-643.16648}  (backed up)
Iteration 44:{space 2}log likelihood = {res:-643.16575}  (not concave)
Iteration 45:{space 2}log likelihood = {res:-643.16571}  
Iteration 46:{space 2}log likelihood = {res:-643.16432}  (not concave)
Iteration 47:{space 2}log likelihood = {res:-643.16425}  
Iteration 48:{space 2}log likelihood = {res:-643.15495}  
Iteration 49:{space 2}log likelihood = {res:-643.14293}  
Iteration 50:{space 2}log likelihood = {res:-643.14293}  (not concave)
Iteration 51:{space 2}log likelihood = {res:-643.14293}  (not concave)
Iteration 52:{space 2}log likelihood = {res:-643.14293}  (not concave)
Iteration 53:{space 2}log likelihood = {res:-643.14293}  (backed up)
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     1,713
{txt}Group variable: {col 27}{res}ccode{col 49}{txt}Number of groups{col 67}={res}{col 69}        28

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        14
{col 63}{txt}avg{col 67}={res}{col 69}      61.2
{col 63}{txt}max{col 67}={res}{col 69}       235

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}16{txt}){col 67}={res}{col 70}   579.48
{txt}Log likelihood = {res}-643.14293{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  prosanctions{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
loggedmigrants {c |}{col 16}{res}{space 2}-.0924397{col 28}{space 2} .0422398{col 39}{space 1}   -2.19{col 48}{space 3}0.029{col 56}{space 4}-.1752281{col 69}{space 3}-.0096512
{txt}{space 4}population {c |}{col 16}{res}{space 2}-.0024574{col 28}{space 2} .0035578{col 39}{space 1}   -0.69{col 48}{space 3}0.490{col 56}{space 4}-.0094305{col 69}{space 3} .0045157
{txt}{space 2}unemployment {c |}{col 16}{res}{space 2}-.0571528{col 28}{space 2} .0181207{col 39}{space 1}   -3.15{col 48}{space 3}0.002{col 56}{space 4}-.0926686{col 69}{space 3}-.0216369
{txt}{space 5}gdpgrowth {c |}{col 16}{res}{space 2}-.0016446{col 28}{space 2} .0518487{col 39}{space 1}   -0.03{col 48}{space 3}0.975{col 56}{space 4}-.1032662{col 69}{space 3}  .099977
{txt}{space 6}distance {c |}{col 16}{res}{space 2} .0002078{col 28}{space 2} .0001211{col 39}{space 1}    1.72{col 48}{space 3}0.086{col 56}{space 4}-.0000295{col 69}{space 3} .0004451
{txt}{space 11}col {c |}{col 16}{res}{space 2} .1280268{col 28}{space 2} .3106355{col 39}{space 1}    0.41{col 48}{space 3}0.680{col 56}{space 4}-.4808075{col 69}{space 3} .7368612
{txt}{space 2}RWPvoteshare {c |}{col 16}{res}{space 2}-.0189523{col 28}{space 2} .0136906{col 39}{space 1}   -1.38{col 48}{space 3}0.166{col 56}{space 4}-.0457853{col 69}{space 3} .0078808
{txt}{space 5}libyabill {c |}{col 16}{res}{space 2} 3.836189{col 28}{space 2} .2276442{col 39}{space 1}   16.85{col 48}{space 3}0.000{col 56}{space 4} 3.390015{col 69}{space 3} 4.282364
{txt}{space 6}iranbill {c |}{col 16}{res}{space 2} 3.748838{col 28}{space 2} .2532281{col 39}{space 1}   14.80{col 48}{space 3}0.000{col 56}{space 4} 3.252521{col 69}{space 3} 4.245156
{txt}{space 14} {c |}
{space 9}party {c |}
{space 10}EPP  {c |}{col 16}{res}{space 2} 2.164169{col 28}{space 2} .3032149{col 39}{space 1}    7.14{col 48}{space 3}0.000{col 56}{space 4} 1.569879{col 69}{space 3} 2.758459
{txt}{space 10}S&D  {c |}{col 16}{res}{space 2} 2.174291{col 28}{space 2}  .309292{col 39}{space 1}    7.03{col 48}{space 3}0.000{col 56}{space 4}  1.56809{col 69}{space 3} 2.780492
{txt}{space 3}Greens/EFA  {c |}{col 16}{res}{space 2}   -.8161{col 28}{space 2} .3521574{col 39}{space 1}   -2.32{col 48}{space 3}0.020{col 56}{space 4}-1.506316{col 69}{space 3}-.1258841
{txt}{space 4}ALDE/ADLE  {c |}{col 16}{res}{space 2} 2.278118{col 28}{space 2} .3575515{col 39}{space 1}    6.37{col 48}{space 3}0.000{col 56}{space 4} 1.577329{col 69}{space 3} 2.978906
{txt}{space 10}ECR  {c |}{col 16}{res}{space 2} 4.042348{col 28}{space 2}  .799239{col 39}{space 1}    5.06{col 48}{space 3}0.000{col 56}{space 4} 2.475869{col 69}{space 3} 5.608828
{txt}{space 9}EFDD  {c |}{col 16}{res}{space 2} .1606211{col 28}{space 2} .4507201{col 39}{space 1}    0.36{col 48}{space 3}0.722{col 56}{space 4}-.7227739{col 69}{space 3} 1.044016
{txt}{space 6}GUE-NGL  {c |}{col 16}{res}{space 2}-.8494492{col 28}{space 2} .3774151{col 39}{space 1}   -2.25{col 48}{space 3}0.024{col 56}{space 4}-1.589169{col 69}{space 3}-.1097292
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2}-2.980598{col 28}{space 2} .6228084{col 39}{space 1}   -4.79{col 48}{space 3}0.000{col 56}{space 4} -4.20128{col 69}{space 3}-1.759916
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}ccode         {col 16}{txt}{c |}
{space 5}var(_cons){c |}{col 16}{res}{space 2} 6.14e-34{col 28}{space 2} 2.45e-18{col 56}{space 4}        .{col 69}{space 3}        .
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}chi2({res}0{txt}) ={res} 0.00{col 59}{txt}Prob > chi2 ={res}{col 73}     .

{txt}{p 0 6 4 79}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}

{com}. estimates store MODELA50, title((A50))
{txt}
{com}. //Model A51
. melogit prosanctions loggedmigrants population unemployment gdpgrowth distance col exportsGDP importsGDP libyabill iranbill i.party || ccode:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-655.65477}  
Iteration 1:{space 3}log likelihood = {res:-639.63473}  
Iteration 2:{space 3}log likelihood = {res:-638.97539}  
Iteration 3:{space 3}log likelihood = {res:-638.97157}  
Iteration 4:{space 3}log likelihood = {res:-638.97157}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-652.36168}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-652.36168}  (not concave)
Iteration 1:{space 3}log likelihood = {res:-643.31147}  (not concave)
Iteration 2:{space 3}log likelihood = {res:-639.79464}  
Iteration 3:{space 3}log likelihood = {res:-638.94655}  
Iteration 4:{space 3}log likelihood = {res: -638.7287}  
Iteration 5:{space 3}log likelihood = {res:-638.72846}  
Iteration 6:{space 3}log likelihood = {res:-638.72846}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     1,699
{txt}Group variable: {col 27}{res}ccode{col 49}{txt}Number of groups{col 67}={res}{col 69}        27

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        14
{col 63}{txt}avg{col 67}={res}{col 69}      62.9
{col 63}{txt}max{col 67}={res}{col 69}       235

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}17{txt}){col 67}={res}{col 70}   564.78
{txt}Log likelihood = {res}-638.72846{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  prosanctions{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
loggedmigrants {c |}{col 16}{res}{space 2}-.1130089{col 28}{space 2}  .045618{col 39}{space 1}   -2.48{col 48}{space 3}0.013{col 56}{space 4}-.2024185{col 69}{space 3}-.0235992
{txt}{space 4}population {c |}{col 16}{res}{space 2}-.0001471{col 28}{space 2} .0037649{col 39}{space 1}   -0.04{col 48}{space 3}0.969{col 56}{space 4}-.0075261{col 69}{space 3} .0072319
{txt}{space 2}unemployment {c |}{col 16}{res}{space 2}-.0510883{col 28}{space 2} .0205781{col 39}{space 1}   -2.48{col 48}{space 3}0.013{col 56}{space 4}-.0914206{col 69}{space 3}-.0107561
{txt}{space 5}gdpgrowth {c |}{col 16}{res}{space 2} .0187762{col 28}{space 2} .0545073{col 39}{space 1}    0.34{col 48}{space 3}0.730{col 56}{space 4}-.0880563{col 69}{space 3} .1256086
{txt}{space 6}distance {c |}{col 16}{res}{space 2} .0002335{col 28}{space 2} .0001407{col 39}{space 1}    1.66{col 48}{space 3}0.097{col 56}{space 4}-.0000424{col 69}{space 3} .0005093
{txt}{space 11}col {c |}{col 16}{res}{space 2} .3240338{col 28}{space 2} .3410022{col 39}{space 1}    0.95{col 48}{space 3}0.342{col 56}{space 4}-.3443183{col 69}{space 3} .9923859
{txt}{space 4}exportsGDP {c |}{col 16}{res}{space 2} 112.2401{col 28}{space 2} 162.7345{col 39}{space 1}    0.69{col 48}{space 3}0.490{col 56}{space 4}-206.7136{col 69}{space 3} 431.1939
{txt}{space 4}importsGDP {c |}{col 16}{res}{space 2}-294.9475{col 28}{space 2} 315.7222{col 39}{space 1}   -0.93{col 48}{space 3}0.350{col 56}{space 4}-913.7517{col 69}{space 3} 323.8567
{txt}{space 5}libyabill {c |}{col 16}{res}{space 2} 3.848089{col 28}{space 2} .2452032{col 39}{space 1}   15.69{col 48}{space 3}0.000{col 56}{space 4}   3.3675{col 69}{space 3} 4.328679
{txt}{space 6}iranbill {c |}{col 16}{res}{space 2} 3.685635{col 28}{space 2} .2745882{col 39}{space 1}   13.42{col 48}{space 3}0.000{col 56}{space 4} 3.147452{col 69}{space 3} 4.223818
{txt}{space 14} {c |}
{space 9}party {c |}
{space 10}EPP  {c |}{col 16}{res}{space 2} 2.161041{col 28}{space 2} .3069553{col 39}{space 1}    7.04{col 48}{space 3}0.000{col 56}{space 4}  1.55942{col 69}{space 3} 2.762663
{txt}{space 10}S&D  {c |}{col 16}{res}{space 2} 2.179926{col 28}{space 2} .3123373{col 39}{space 1}    6.98{col 48}{space 3}0.000{col 56}{space 4} 1.567756{col 69}{space 3} 2.792096
{txt}{space 3}Greens/EFA  {c |}{col 16}{res}{space 2}-.7911706{col 28}{space 2} .3579058{col 39}{space 1}   -2.21{col 48}{space 3}0.027{col 56}{space 4}-1.492653{col 69}{space 3}-.0896881
{txt}{space 4}ALDE/ADLE  {c |}{col 16}{res}{space 2} 2.230836{col 28}{space 2} .3600278{col 39}{space 1}    6.20{col 48}{space 3}0.000{col 56}{space 4} 1.525195{col 69}{space 3} 2.936478
{txt}{space 10}ECR  {c |}{col 16}{res}{space 2} 4.091872{col 28}{space 2} .7999641{col 39}{space 1}    5.12{col 48}{space 3}0.000{col 56}{space 4} 2.523971{col 69}{space 3} 5.659773
{txt}{space 9}EFDD  {c |}{col 16}{res}{space 2} .1775254{col 28}{space 2} .4567982{col 39}{space 1}    0.39{col 48}{space 3}0.698{col 56}{space 4}-.7177826{col 69}{space 3} 1.072833
{txt}{space 6}GUE-NGL  {c |}{col 16}{res}{space 2}-.8398854{col 28}{space 2} .3859228{col 39}{space 1}   -2.18{col 48}{space 3}0.030{col 56}{space 4} -1.59628{col 69}{space 3}-.0834906
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2}-3.147101{col 28}{space 2} .6412872{col 39}{space 1}   -4.91{col 48}{space 3}0.000{col 56}{space 4}   -4.404{col 69}{space 3}-1.890201
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}ccode         {col 16}{txt}{c |}
{space 5}var(_cons){c |}{col 16}{res}{space 2} .0239608{col 28}{space 2} .0420431{col 56}{space 4}  .000769{col 69}{space 3} .7465713
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 0.49{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.2428
{txt}
{com}. estimates store MODELA51, title((A51))
{txt}
{com}. //Model A52
. melogit prosanctions loggedmigrants population unemployment gdpgrowth distance col pincometax sstax libyabill iranbill  i.party || ccode:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-614.09528}  
Iteration 1:{space 3}log likelihood = {res:-601.39302}  
Iteration 2:{space 3}log likelihood = {res:-600.81994}  
Iteration 3:{space 3}log likelihood = {res:-600.81831}  
Iteration 4:{space 3}log likelihood = {res:-600.81831}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res: -613.9251}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: -613.9251}  (not concave)
Iteration 1:{space 3}log likelihood = {res:-604.86623}  (not concave)
Iteration 2:{space 3}log likelihood = {res: -601.4788}  
Iteration 3:{space 3}log likelihood = {res:-600.86064}  
Iteration 4:{space 3}log likelihood = {res:-600.85491}  
Iteration 5:{space 3}log likelihood = {res:-600.84991}  
Iteration 6:{space 3}log likelihood = {res:-600.84963}  (backed up)
Iteration 7:{space 3}log likelihood = {res:-600.84956}  (backed up)
Iteration 8:{space 3}log likelihood = {res:-600.84954}  (backed up)
Iteration 9:{space 3}log likelihood = {res:-600.84954}  (backed up)
Iteration 10:{space 2}log likelihood = {res:-600.84954}  (backed up)
Iteration 11:{space 2}log likelihood = {res:-600.84954}  (backed up)
Iteration 12:{space 2}log likelihood = {res:-600.84954}  (backed up)
Iteration 13:{space 2}log likelihood = {res:-600.84954}  (backed up)
Iteration 14:{space 2}log likelihood = {res:-600.84954}  (backed up)
Iteration 15:{space 2}log likelihood = {res:-600.84954}  (backed up)
Iteration 16:{space 2}log likelihood = {res:-600.84954}  (backed up)
Iteration 17:{space 2}log likelihood = {res:-600.84954}  (not concave)
Iteration 18:{space 2}log likelihood = {res:-600.84954}  (backed up)
Iteration 19:{space 2}log likelihood = {res:-600.84954}  (not concave)
Iteration 20:{space 2}log likelihood = {res:-600.84954}  (backed up)
Iteration 21:{space 2}log likelihood = {res:-600.84954}  (backed up)
Iteration 22:{space 2}log likelihood = {res:-600.84954}  (backed up)
Iteration 23:{space 2}log likelihood = {res:-600.84954}  (backed up)
Iteration 24:{space 2}log likelihood = {res:-600.84954}  (backed up)
Iteration 25:{space 2}log likelihood = {res:-600.84954}  (not concave)
Iteration 26:{space 2}log likelihood = {res:-600.84954}  (not concave)
Iteration 27:{space 2}log likelihood = {res:-600.84954}  (backed up)
Iteration 28:{space 2}log likelihood = {res:-600.84954}  (backed up)
Iteration 29:{space 2}log likelihood = {res:-600.84954}  (backed up)
Iteration 30:{space 2}log likelihood = {res:-600.84954}  (not concave)
Iteration 31:{space 2}log likelihood = {res:-600.84954}  (not concave)
Iteration 32:{space 2}log likelihood = {res:-600.84954}  (not concave)
Iteration 33:{space 2}log likelihood = {res:-600.84954}  
Iteration 34:{space 2}log likelihood = {res:-600.84947}  (not concave)
Iteration 35:{space 2}log likelihood = {res:-600.84947}  
Iteration 36:{space 2}log likelihood = {res:-600.84851}  (backed up)
Iteration 37:{space 2}log likelihood = {res:-600.84804}  (not concave)
Iteration 38:{space 2}log likelihood = {res:-600.84802}  (not concave)
Iteration 39:{space 2}log likelihood = {res:-600.84801}  (not concave)
Iteration 40:{space 2}log likelihood = {res:-600.84791}  (not concave)
Iteration 41:{space 2}log likelihood = {res:-600.84787}  (not concave)
Iteration 42:{space 2}log likelihood = {res:-600.84777}  
Iteration 43:{space 2}log likelihood = {res:-600.81831}  
Iteration 44:{space 2}log likelihood = {res:-600.81831}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     1,522
{txt}Group variable: {col 27}{res}ccode{col 49}{txt}Number of groups{col 67}={res}{col 69}        22

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        14
{col 63}{txt}avg{col 67}={res}{col 69}      69.2
{col 63}{txt}max{col 67}={res}{col 69}       235

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}17{txt}){col 67}={res}{col 70}   498.04
{txt}Log likelihood = {res}-600.81831{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  prosanctions{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
loggedmigrants {c |}{col 16}{res}{space 2} -.083597{col 28}{space 2} .0431969{col 39}{space 1}   -1.94{col 48}{space 3}0.053{col 56}{space 4}-.1682614{col 69}{space 3} .0010674
{txt}{space 4}population {c |}{col 16}{res}{space 2}-.0001254{col 28}{space 2} .0034835{col 39}{space 1}   -0.04{col 48}{space 3}0.971{col 56}{space 4}-.0069529{col 69}{space 3} .0067021
{txt}{space 2}unemployment {c |}{col 16}{res}{space 2}-.0563771{col 28}{space 2} .0186985{col 39}{space 1}   -3.02{col 48}{space 3}0.003{col 56}{space 4}-.0930255{col 69}{space 3}-.0197288
{txt}{space 5}gdpgrowth {c |}{col 16}{res}{space 2}-.0397195{col 28}{space 2} .0555218{col 39}{space 1}   -0.72{col 48}{space 3}0.474{col 56}{space 4}-.1485402{col 69}{space 3} .0691012
{txt}{space 6}distance {c |}{col 16}{res}{space 2} .0002298{col 28}{space 2} .0001423{col 39}{space 1}    1.61{col 48}{space 3}0.106{col 56}{space 4}-.0000491{col 69}{space 3} .0005087
{txt}{space 11}col {c |}{col 16}{res}{space 2} .1637576{col 28}{space 2} .3093092{col 39}{space 1}    0.53{col 48}{space 3}0.597{col 56}{space 4}-.4424774{col 69}{space 3} .7699925
{txt}{space 4}pincometax {c |}{col 16}{res}{space 2}-.0307171{col 28}{space 2} .0254683{col 39}{space 1}   -1.21{col 48}{space 3}0.228{col 56}{space 4} -.080634{col 69}{space 3} .0191999
{txt}{space 9}sstax {c |}{col 16}{res}{space 2}-.0256418{col 28}{space 2} .0251516{col 39}{space 1}   -1.02{col 48}{space 3}0.308{col 56}{space 4}-.0749381{col 69}{space 3} .0236544
{txt}{space 5}libyabill {c |}{col 16}{res}{space 2} 3.641696{col 28}{space 2}   .24271{col 39}{space 1}   15.00{col 48}{space 3}0.000{col 56}{space 4} 3.165994{col 69}{space 3} 4.117399
{txt}{space 6}iranbill {c |}{col 16}{res}{space 2} 3.492766{col 28}{space 2}  .280077{col 39}{space 1}   12.47{col 48}{space 3}0.000{col 56}{space 4} 2.943825{col 69}{space 3} 4.041707
{txt}{space 14} {c |}
{space 9}party {c |}
{space 10}EPP  {c |}{col 16}{res}{space 2} 2.242695{col 28}{space 2}  .306726{col 39}{space 1}    7.31{col 48}{space 3}0.000{col 56}{space 4} 1.641523{col 69}{space 3} 2.843867
{txt}{space 10}S&D  {c |}{col 16}{res}{space 2} 2.244474{col 28}{space 2} .3144906{col 39}{space 1}    7.14{col 48}{space 3}0.000{col 56}{space 4} 1.628084{col 69}{space 3} 2.860865
{txt}{space 3}Greens/EFA  {c |}{col 16}{res}{space 2}-.6296552{col 28}{space 2} .3556253{col 39}{space 1}   -1.77{col 48}{space 3}0.077{col 56}{space 4}-1.326668{col 69}{space 3} .0673575
{txt}{space 4}ALDE/ADLE  {c |}{col 16}{res}{space 2} 2.419861{col 28}{space 2}  .371377{col 39}{space 1}    6.52{col 48}{space 3}0.000{col 56}{space 4} 1.691976{col 69}{space 3} 3.147747
{txt}{space 10}ECR  {c |}{col 16}{res}{space 2} 4.152481{col 28}{space 2} .8015929{col 39}{space 1}    5.18{col 48}{space 3}0.000{col 56}{space 4} 2.581388{col 69}{space 3} 5.723574
{txt}{space 9}EFDD  {c |}{col 16}{res}{space 2} .3199537{col 28}{space 2} .4667343{col 39}{space 1}    0.69{col 48}{space 3}0.493{col 56}{space 4}-.5948287{col 69}{space 3} 1.234736
{txt}{space 6}GUE-NGL  {c |}{col 16}{res}{space 2} -.703584{col 28}{space 2} .3847136{col 39}{space 1}   -1.83{col 48}{space 3}0.067{col 56}{space 4}-1.457609{col 69}{space 3} .0504409
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2}-2.650842{col 28}{space 2} .8541904{col 39}{space 1}   -3.10{col 48}{space 3}0.002{col 56}{space 4}-4.325025{col 69}{space 3}-.9766599
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}ccode         {col 16}{txt}{c |}
{space 5}var(_cons){c |}{col 16}{res}{space 2} 2.84e-31{col 28}{space 2} 1.43e-15{col 56}{space 4}        .{col 69}{space 3}        .
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}chi2({res}0{txt}) ={res} 0.00{col 59}{txt}Prob > chi2 ={res}{col 73}     .

{txt}{p 0 6 4 79}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}

{com}. estimates store MODELA52, title((A52))
{txt}
{com}. //Model A53
. melogit prosanctions loggedmigrants population unemployment gdpgrowth distance col logged_refugee libyabill iranbill i.party || ccode:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-619.71612}  
Iteration 1:{space 3}log likelihood = {res: -603.8199}  
Iteration 2:{space 3}log likelihood = {res:-603.17145}  
Iteration 3:{space 3}log likelihood = {res:-603.16769}  
Iteration 4:{space 3}log likelihood = {res:-603.16769}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-615.93546}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-615.93546}  (not concave)
Iteration 1:{space 3}log likelihood = {res:-607.39173}  (not concave)
Iteration 2:{space 3}log likelihood = {res:-604.06178}  
Iteration 3:{space 3}log likelihood = {res:-603.22644}  
Iteration 4:{space 3}log likelihood = {res:-603.11173}  
Iteration 5:{space 3}log likelihood = {res:-603.11018}  
Iteration 6:{space 3}log likelihood = {res:-603.11017}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     1,621
{txt}Group variable: {col 27}{res}ccode{col 49}{txt}Number of groups{col 67}={res}{col 69}        26

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         6
{col 63}{txt}avg{col 67}={res}{col 69}      62.3
{col 63}{txt}max{col 67}={res}{col 69}       235

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}16{txt}){col 67}={res}{col 70}   548.22
{txt}Log likelihood = {res}-603.11017{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   prosanctions{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}loggedmigrants {c |}{col 17}{res}{space 2}-.1770931{col 29}{space 2} .0651296{col 40}{space 1}   -2.72{col 49}{space 3}0.007{col 57}{space 4}-.3047447{col 70}{space 3}-.0494416
{txt}{space 5}population {c |}{col 17}{res}{space 2}-.0014699{col 29}{space 2} .0037779{col 40}{space 1}   -0.39{col 49}{space 3}0.697{col 57}{space 4}-.0088744{col 70}{space 3} .0059346
{txt}{space 3}unemployment {c |}{col 17}{res}{space 2}-.0535545{col 29}{space 2} .0200167{col 40}{space 1}   -2.68{col 49}{space 3}0.007{col 57}{space 4}-.0927864{col 70}{space 3}-.0143226
{txt}{space 6}gdpgrowth {c |}{col 17}{res}{space 2}-.0002836{col 29}{space 2}  .056875{col 40}{space 1}   -0.00{col 49}{space 3}0.996{col 57}{space 4}-.1117567{col 70}{space 3} .1111894
{txt}{space 7}distance {c |}{col 17}{res}{space 2} .0002164{col 29}{space 2} .0001353{col 40}{space 1}    1.60{col 49}{space 3}0.110{col 57}{space 4}-.0000488{col 70}{space 3} .0004816
{txt}{space 12}col {c |}{col 17}{res}{space 2} .3297755{col 29}{space 2} .3451228{col 40}{space 1}    0.96{col 49}{space 3}0.339{col 57}{space 4}-.3466527{col 70}{space 3} 1.006204
{txt}logged_refugees {c |}{col 17}{res}{space 2} .0950695{col 29}{space 2} .0719918{col 40}{space 1}    1.32{col 49}{space 3}0.187{col 57}{space 4}-.0460317{col 70}{space 3} .2361708
{txt}{space 6}libyabill {c |}{col 17}{res}{space 2} 3.820384{col 29}{space 2} .2408753{col 40}{space 1}   15.86{col 49}{space 3}0.000{col 57}{space 4} 3.348277{col 70}{space 3} 4.292491
{txt}{space 7}iranbill {c |}{col 17}{res}{space 2} 3.662486{col 29}{space 2} .2822701{col 40}{space 1}   12.98{col 49}{space 3}0.000{col 57}{space 4} 3.109247{col 70}{space 3} 4.215725
{txt}{space 15} {c |}
{space 10}party {c |}
{space 11}EPP  {c |}{col 17}{res}{space 2} 2.234607{col 29}{space 2} .3092225{col 40}{space 1}    7.23{col 49}{space 3}0.000{col 57}{space 4} 1.628543{col 70}{space 3} 2.840672
{txt}{space 11}S&D  {c |}{col 17}{res}{space 2} 2.220762{col 29}{space 2} .3131861{col 40}{space 1}    7.09{col 49}{space 3}0.000{col 57}{space 4} 1.606929{col 70}{space 3} 2.834596
{txt}{space 4}Greens/EFA  {c |}{col 17}{res}{space 2}-.9126113{col 29}{space 2} .3625065{col 40}{space 1}   -2.52{col 49}{space 3}0.012{col 57}{space 4}-1.623111{col 70}{space 3}-.2021116
{txt}{space 5}ALDE/ADLE  {c |}{col 17}{res}{space 2} 2.259861{col 29}{space 2} .3670222{col 40}{space 1}    6.16{col 49}{space 3}0.000{col 57}{space 4} 1.540511{col 70}{space 3} 2.979212
{txt}{space 11}ECR  {c |}{col 17}{res}{space 2} 4.134526{col 29}{space 2} .8087214{col 40}{space 1}    5.11{col 49}{space 3}0.000{col 57}{space 4} 2.549461{col 70}{space 3} 5.719591
{txt}{space 10}EFDD  {c |}{col 17}{res}{space 2} .1305593{col 29}{space 2} .4675825{col 40}{space 1}    0.28{col 49}{space 3}0.780{col 57}{space 4}-.7858855{col 70}{space 3} 1.047004
{txt}{space 7}GUE-NGL  {c |}{col 17}{res}{space 2}-1.120831{col 29}{space 2} .3998531{col 40}{space 1}   -2.80{col 49}{space 3}0.005{col 57}{space 4}-1.904528{col 70}{space 3}-.3371333
{txt}{space 15} {c |}
{space 10}_cons {c |}{col 17}{res}{space 2}-3.005449{col 29}{space 2} .6576385{col 40}{space 1}   -4.57{col 49}{space 3}0.000{col 57}{space 4}-4.294397{col 70}{space 3}-1.716501
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}ccode          {col 17}{txt}{c |}
{space 6}var(_cons){c |}{col 17}{res}{space 2} .0123751{col 29}{space 2}  .040407{col 57}{space 4} .0000206{col 70}{space 3} 7.445416
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 0.12{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.3672
{txt}
{com}. estimates store MODELA53, title((A53))
{txt}
{com}. //Model A54
. melogit prosanctions loggedmigrants population unemployment gdpgrowth distance col lnremit libyabill iranbill i.party || ccode:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-632.81151}  
Iteration 1:{space 3}log likelihood = {res:-619.74178}  
Iteration 2:{space 3}log likelihood = {res:-619.22548}  
Iteration 3:{space 3}log likelihood = {res:-619.22291}  
Iteration 4:{space 3}log likelihood = {res:-619.22291}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-632.56051}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-632.56051}  (not concave)
Iteration 1:{space 3}log likelihood = {res: -623.7236}  (not concave)
Iteration 2:{space 3}log likelihood = {res:-620.24834}  
Iteration 3:{space 3}log likelihood = {res:-619.30504}  
Iteration 4:{space 3}log likelihood = {res:-619.10835}  
Iteration 5:{space 3}log likelihood = {res:-619.10641}  
Iteration 6:{space 3}log likelihood = {res: -619.1064}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     1,576
{txt}Group variable: {col 27}{res}ccode{col 49}{txt}Number of groups{col 67}={res}{col 69}        27

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         9
{col 63}{txt}avg{col 67}={res}{col 69}      58.4
{col 63}{txt}max{col 67}={res}{col 69}       235

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}16{txt}){col 67}={res}{col 70}   515.72
{txt}Log likelihood = {res} -619.1064{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  prosanctions{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
loggedmigrants {c |}{col 16}{res}{space 2}-.0732102{col 28}{space 2} .0485902{col 39}{space 1}   -1.51{col 48}{space 3}0.132{col 56}{space 4}-.1684452{col 69}{space 3} .0220248
{txt}{space 4}population {c |}{col 16}{res}{space 2} .0030711{col 28}{space 2} .0045156{col 39}{space 1}    0.68{col 48}{space 3}0.496{col 56}{space 4}-.0057793{col 69}{space 3} .0119216
{txt}{space 2}unemployment {c |}{col 16}{res}{space 2}-.0526053{col 28}{space 2} .0190935{col 39}{space 1}   -2.76{col 48}{space 3}0.006{col 56}{space 4}-.0900278{col 69}{space 3}-.0151828
{txt}{space 5}gdpgrowth {c |}{col 16}{res}{space 2} .0236648{col 28}{space 2}  .056214{col 39}{space 1}    0.42{col 48}{space 3}0.674{col 56}{space 4}-.0865127{col 69}{space 3} .1338422
{txt}{space 6}distance {c |}{col 16}{res}{space 2} .0002653{col 28}{space 2} .0001277{col 39}{space 1}    2.08{col 48}{space 3}0.038{col 56}{space 4} .0000149{col 69}{space 3} .0005156
{txt}{space 11}col {c |}{col 16}{res}{space 2} .2604904{col 28}{space 2} .3374162{col 39}{space 1}    0.77{col 48}{space 3}0.440{col 56}{space 4}-.4008332{col 69}{space 3}  .921814
{txt}{space 7}lnremit {c |}{col 16}{res}{space 2}-.1248553{col 28}{space 2} .0920408{col 39}{space 1}   -1.36{col 48}{space 3}0.175{col 56}{space 4} -.305252{col 69}{space 3} .0555414
{txt}{space 5}libyabill {c |}{col 16}{res}{space 2} 3.694583{col 28}{space 2} .2411254{col 39}{space 1}   15.32{col 48}{space 3}0.000{col 56}{space 4} 3.221986{col 69}{space 3}  4.16718
{txt}{space 6}iranbill {c |}{col 16}{res}{space 2} 3.597614{col 28}{space 2} .2603058{col 39}{space 1}   13.82{col 48}{space 3}0.000{col 56}{space 4} 3.087424{col 69}{space 3} 4.107804
{txt}{space 14} {c |}
{space 9}party {c |}
{space 10}EPP  {c |}{col 16}{res}{space 2}   1.9949{col 28}{space 2} .3094673{col 39}{space 1}    6.45{col 48}{space 3}0.000{col 56}{space 4} 1.388355{col 69}{space 3} 2.601445
{txt}{space 10}S&D  {c |}{col 16}{res}{space 2} 2.046544{col 28}{space 2} .3134359{col 39}{space 1}    6.53{col 48}{space 3}0.000{col 56}{space 4} 1.432221{col 69}{space 3} 2.660867
{txt}{space 3}Greens/EFA  {c |}{col 16}{res}{space 2}-.7777808{col 28}{space 2} .3577954{col 39}{space 1}   -2.17{col 48}{space 3}0.030{col 56}{space 4}-1.479047{col 69}{space 3}-.0765147
{txt}{space 4}ALDE/ADLE  {c |}{col 16}{res}{space 2} 2.128792{col 28}{space 2}  .362172{col 39}{space 1}    5.88{col 48}{space 3}0.000{col 56}{space 4} 1.418948{col 69}{space 3} 2.838636
{txt}{space 10}ECR  {c |}{col 16}{res}{space 2} 3.886517{col 28}{space 2}  .799771{col 39}{space 1}    4.86{col 48}{space 3}0.000{col 56}{space 4} 2.318995{col 69}{space 3} 5.454039
{txt}{space 9}EFDD  {c |}{col 16}{res}{space 2} .0065816{col 28}{space 2} .4666364{col 39}{space 1}    0.01{col 48}{space 3}0.989{col 56}{space 4} -.908009{col 69}{space 3} .9211721
{txt}{space 6}GUE-NGL  {c |}{col 16}{res}{space 2}-.6301055{col 28}{space 2} .3952814{col 39}{space 1}   -1.59{col 48}{space 3}0.111{col 56}{space 4}-1.404843{col 69}{space 3} .1446318
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2}-3.258061{col 28}{space 2} .6450395{col 39}{space 1}   -5.05{col 48}{space 3}0.000{col 56}{space 4}-4.522315{col 69}{space 3}-1.993806
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}ccode         {col 16}{txt}{c |}
{space 5}var(_cons){c |}{col 16}{res}{space 2} .0154144{col 28}{space 2} .0371785{col 56}{space 4} .0001364{col 69}{space 3} 1.741662
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 0.23{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.3146
{txt}
{com}. estimates store MODELA54, title((A54))
{txt}
{com}. 
. estout MODELA47 MODELA48 MODELA49 MODELA50 MODELA51 MODELA52 MODELA53 MODELA54, cells(b(star fmt(3)) se(par fmt(3))) starlevels($^{c -(}+{c )-}$ 0.10 $^{c -(}*{c )-}$ 0.05 $^{c -(}**{c )-}$ 0.01 $^{c -(}***{c )-}$ 0.001) stats(N N_g r2, fmt(0 0 3) label(Observations Countries R$^2$)) label,  using "modelsa47_a54.tex", replace style(tex)
{res}{txt}(note: file modelsa47_a54.tex not found)
(output written to {browse  `"modelsa47_a54.tex"'})

{com}. 
. **TABLE A8
. tab party, gen(partydum)

   {txt}EU Party {c |}
 Membership {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         NI {c |}{res}        114        5.06        5.06
{txt}        EPP {c |}{res}        754       33.47       38.53
{txt}        S&D {c |}{res}        571       25.34       63.87
{txt} Greens/EFA {c |}{res}        163        7.23       71.11
{txt}  ALDE/ADLE {c |}{res}        237       10.52       81.62
{txt}        ECR {c |}{res}        185        8.21       89.84
{txt}       EFDD {c |}{res}        108        4.79       94.63
{txt}    GUE-NGL {c |}{res}        121        5.37      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,253      100.00
{txt}
{com}. medeff (regress RWPvoteshare loggedmigrants population unemployment gdpgrowth distance col libyabill iranbill partydum1 partydum2 partydum3 partydum4 partydum5 partydum6 partydum7) (logit prosanctions loggedmigrants RWPvoteshare population unemployment gdpgrowth distance col libyabill iranbill partydum1 partydum2 partydum3 partydum4 partydum5 partydum6 partydum7), mediate(RWPvoteshare) treat(loggedmigrants 7.543026 10.486297) sims(1000) seed(23145) vce(cluster ccode) level(90)

{txt}Linear regression                               Number of obs     = {res}     1,713
                                                {txt}F(15, 27)         =  {res}    11.86
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3087
                                                {txt}Root MSE          =    {res} 5.2851

{txt}{ralign 80:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}  RWPvoteshare{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      t{col 48}   P>|t|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
loggedmigrants {c |}{col 16}{res}{space 2} .7583367{col 28}{space 2} .2912206{col 39}{space 1}    2.60{col 48}{space 3}0.015{col 56}{space 4} .1608014{col 69}{space 3} 1.355872
{txt}{space 4}population {c |}{col 16}{res}{space 2}-.0994094{col 28}{space 2} .0379895{col 39}{space 1}   -2.62{col 48}{space 3}0.014{col 56}{space 4}-.1773574{col 69}{space 3}-.0214614
{txt}{space 2}unemployment {c |}{col 16}{res}{space 2}-.2367519{col 28}{space 2} .1472553{col 39}{space 1}   -1.61{col 48}{space 3}0.120{col 56}{space 4}-.5388949{col 69}{space 3} .0653911
{txt}{space 5}gdpgrowth {c |}{col 16}{res}{space 2}-.6684044{col 28}{space 2} .4600324{col 39}{space 1}   -1.45{col 48}{space 3}0.158{col 56}{space 4}-1.612313{col 69}{space 3} .2755041
{txt}{space 6}distance {c |}{col 16}{res}{space 2}-.0008703{col 28}{space 2} .0011693{col 39}{space 1}   -0.74{col 48}{space 3}0.463{col 56}{space 4}-.0032695{col 69}{space 3}  .001529
{txt}{space 11}col {c |}{col 16}{res}{space 2}-2.065654{col 28}{space 2} 2.119317{col 39}{space 1}   -0.97{col 48}{space 3}0.338{col 56}{space 4}-6.414133{col 69}{space 3} 2.282824
{txt}{space 5}libyabill {c |}{col 16}{res}{space 2}  2.41433{col 28}{space 2}  1.60747{col 39}{space 1}    1.50{col 48}{space 3}0.145{col 56}{space 4}-.8839247{col 69}{space 3} 5.712585
{txt}{space 6}iranbill {c |}{col 16}{res}{space 2} .8830827{col 28}{space 2} 1.770348{col 39}{space 1}    0.50{col 48}{space 3}0.622{col 56}{space 4}-2.749371{col 69}{space 3} 4.515537
{txt}{space 5}partydum1 {c |}{col 16}{res}{space 2} 4.811453{col 28}{space 2} 1.609265{col 39}{space 1}    2.99{col 48}{space 3}0.006{col 56}{space 4} 1.509514{col 69}{space 3} 8.113392
{txt}{space 5}partydum2 {c |}{col 16}{res}{space 2} .2712588{col 28}{space 2} 1.056177{col 39}{space 1}    0.26{col 48}{space 3}0.799{col 56}{space 4}-1.895837{col 69}{space 3} 2.438354
{txt}{space 5}partydum3 {c |}{col 16}{res}{space 2}-.7306935{col 28}{space 2} .9446061{col 39}{space 1}   -0.77{col 48}{space 3}0.446{col 56}{space 4}-2.668865{col 69}{space 3} 1.207478
{txt}{space 5}partydum4 {c |}{col 16}{res}{space 2} 2.000753{col 28}{space 2} 1.168844{col 39}{space 1}    1.71{col 48}{space 3}0.098{col 56}{space 4} -.397517{col 69}{space 3} 4.399022
{txt}{space 5}partydum5 {c |}{col 16}{res}{space 2}-.4809171{col 28}{space 2} .9159126{col 39}{space 1}   -0.53{col 48}{space 3}0.604{col 56}{space 4}-2.360214{col 69}{space 3}  1.39838
{txt}{space 5}partydum6 {c |}{col 16}{res}{space 2}  -2.9483{col 28}{space 2} 1.500257{col 39}{space 1}   -1.97{col 48}{space 3}0.060{col 56}{space 4}-6.026574{col 69}{space 3} .1299734
{txt}{space 5}partydum7 {c |}{col 16}{res}{space 2}-.9335056{col 28}{space 2} 1.627121{col 39}{space 1}   -0.57{col 48}{space 3}0.571{col 56}{space 4}-4.272082{col 69}{space 3} 2.405071
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}  8.60078{col 28}{space 2} 5.098142{col 39}{space 1}    1.69{col 48}{space 3}0.103{col 56}{space 4}-1.859743{col 69}{space 3}  19.0613
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Iteration 0:   log pseudolikelihood = {res}-1172.2762
{txt}Iteration 1:   log pseudolikelihood = {res}-685.99585
{txt}Iteration 2:   log pseudolikelihood = {res}-646.24357
{txt}Iteration 3:   log pseudolikelihood = {res} -643.3303
{txt}Iteration 4:   log pseudolikelihood = {res}-643.14715
{txt}Iteration 5:   log pseudolikelihood = {res}-643.14293
{txt}Iteration 6:   log pseudolikelihood = {res}-643.14293

{txt}Logistic regression                               Number of obs   = {res}      1713
                                                  {txt}Wald chi2({res}16{txt})   = {res}    765.89
                                                  {txt}Prob > chi2     = {res}    0.0000
{txt}Log pseudolikelihood = {res}-643.14293                 {txt}Pseudo R2       = {res}    0.4514

                                 {txt}(Std. Err. adjusted for {res}28{txt} clusters in ccode)
{hline 13}{c TT}{hline 64}
             {c |}               Robust
prosanctions {c |}      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
{hline 13}{c +}{hline 64}
loggedmigr~s {c |}  {res}-.0924468   .0353559    -2.61   0.009     -.161743   -.0231506
{txt}RWPvoteshare {c |}  {res}-.0189526   .0104941    -1.81   0.071    -.0395207    .0016154
  {txt}population {c |}  {res}-.0024571   .0040491    -0.61   0.544    -.0103932    .0054791
{txt}unemployment {c |}  {res}-.0571516   .0203675    -2.81   0.005    -.0970711   -.0172321
   {txt}gdpgrowth {c |}  {res}-.0016216   .0611264    -0.03   0.979    -.1214271     .118184
    {txt}distance {c |}  {res} .0002078   .0001044     1.99   0.047     3.19e-06    .0004124
         {txt}col {c |}  {res} .1280556   .1704476     0.75   0.452    -.2060155    .4621267
   {txt}libyabill {c |}  {res} 3.836285   .3241798    11.83   0.000     3.200904    4.471666
    {txt}iranbill {c |}  {res} 3.748971   .3931612     9.54   0.000     2.978389    4.519553
   {txt}partydum1 {c |}  {res} .8495083   1.128288     0.75   0.451    -1.361895    3.060912
   {txt}partydum2 {c |}  {res} 3.013699   .4308449     6.99   0.000     2.169258    3.858139
   {txt}partydum3 {c |}  {res} 3.023825    .449434     6.73   0.000     2.142951    3.904699
   {txt}partydum4 {c |}  {res}  .033384   .4502039     0.07   0.941    -.8489993    .9157674
   {txt}partydum5 {c |}  {res} 3.127661    .538817     5.80   0.000     2.071599    4.183723
   {txt}partydum6 {c |}  {res} 4.891875   .8416732     5.81   0.000     3.242226    6.541524
   {txt}partydum7 {c |}  {res} 1.010132   .6028843     1.68   0.094    -.1714996    2.191763
       {txt}_cons {c |}  {res}-3.830222   .7348008    -5.21   0.000    -5.270405   -2.390039
{txt}{hline 13}{c BT}{hline 64}
{res}{txt}{hline 25}{c TT}{hline 52}
        Effect           {c |}  Mean           [90% Conf. Interval]
{hline 25}{c +}{hline 52}
{res}        ACME1            {c |} -.0052222     -.0117626     -.0006962
        ACME0            {c |} -.0051667     -.0116661     -.0006782
        Direct Effect 1  {c |} -.0329581     -.0542268     -.0112147
        Direct Effect 0  {c |} -.0329026     -.0541295     -.0112216
        Total Effect     {c |} -.0381249     -.0567823     -.0195789
{txt}{hline 25}{c BT}{hline 52}

{com}. 
. **TABLE A9
. medeff (regress lnremit loggedmigrants population unemployment gdpgrowth distance col libyabill iranbill partydum1 partydum2 partydum3 partydum4 partydum5 partydum6 partydum7) (logit prosanctions loggedmigrants lnremit population unemployment gdpgrowth distance col libyabill iranbill partydum1 partydum2 partydum3 partydum4 partydum5 partydum6 partydum7), mediate(lnremit) treat(loggedmigrants 7.543026 10.486297) sims(1000) seed(23145) vce(cluster ccode) level(90)

{txt}Linear regression                               Number of obs     = {res}     1,576
                                                {txt}F(15, 26)         =  {res}    82.35
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.6698
                                                {txt}Root MSE          =    {res}  .8475

{txt}{ralign 80:(Std. Err. adjusted for {res:27} clusters in ccode)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}       lnremit{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      t{col 48}   P>|t|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
loggedmigrants {c |}{col 16}{res}{space 2} .2033296{col 28}{space 2} .1062898{col 39}{space 1}    1.91{col 48}{space 3}0.067{col 56}{space 4}-.0151522{col 69}{space 3} .4218114
{txt}{space 4}population {c |}{col 16}{res}{space 2}  .026051{col 28}{space 2} .0073797{col 39}{space 1}    3.53{col 48}{space 3}0.002{col 56}{space 4} .0108817{col 69}{space 3} .0412203
{txt}{space 2}unemployment {c |}{col 16}{res}{space 2}-.0237674{col 28}{space 2} .0221325{col 39}{space 1}   -1.07{col 48}{space 3}0.293{col 56}{space 4}-.0692613{col 69}{space 3} .0217266
{txt}{space 5}gdpgrowth {c |}{col 16}{res}{space 2}-.0862871{col 28}{space 2} .0877861{col 39}{space 1}   -0.98{col 48}{space 3}0.335{col 56}{space 4} -.266734{col 69}{space 3} .0941597
{txt}{space 6}distance {c |}{col 16}{res}{space 2} .0000708{col 28}{space 2} .0001754{col 39}{space 1}    0.40{col 48}{space 3}0.690{col 56}{space 4}-.0002898{col 69}{space 3} .0004313
{txt}{space 11}col {c |}{col 16}{res}{space 2}-.2300331{col 28}{space 2} .3173604{col 39}{space 1}   -0.72{col 48}{space 3}0.475{col 56}{space 4}-.8823768{col 69}{space 3} .4223105
{txt}{space 5}libyabill {c |}{col 16}{res}{space 2}-.0396046{col 28}{space 2} .3555062{col 39}{space 1}   -0.11{col 48}{space 3}0.912{col 56}{space 4}-.7703579{col 69}{space 3} .6911488
{txt}{space 6}iranbill {c |}{col 16}{res}{space 2}-.4935253{col 28}{space 2} .3152186{col 39}{space 1}   -1.57{col 48}{space 3}0.130{col 56}{space 4}-1.141466{col 69}{space 3} .1544157
{txt}{space 5}partydum1 {c |}{col 16}{res}{space 2} .0205433{col 28}{space 2} .2443463{col 39}{space 1}    0.08{col 48}{space 3}0.934{col 56}{space 4}-.4817177{col 69}{space 3} .5228043
{txt}{space 5}partydum2 {c |}{col 16}{res}{space 2}-.3440533{col 28}{space 2} .1736492{col 39}{space 1}   -1.98{col 48}{space 3}0.058{col 56}{space 4}-.7009943{col 69}{space 3} .0128878
{txt}{space 5}partydum3 {c |}{col 16}{res}{space 2}-.4039957{col 28}{space 2}  .151059{col 39}{space 1}   -2.67{col 48}{space 3}0.013{col 56}{space 4}-.7145018{col 69}{space 3}-.0934895
{txt}{space 5}partydum4 {c |}{col 16}{res}{space 2} .0073762{col 28}{space 2} .1446348{col 39}{space 1}    0.05{col 48}{space 3}0.960{col 56}{space 4}-.2899249{col 69}{space 3} .3046773
{txt}{space 5}partydum5 {c |}{col 16}{res}{space 2} -.047989{col 28}{space 2}  .178755{col 39}{space 1}   -0.27{col 48}{space 3}0.790{col 56}{space 4}-.4154252{col 69}{space 3} .3194472
{txt}{space 5}partydum6 {c |}{col 16}{res}{space 2}-.8868307{col 28}{space 2} .2303157{col 39}{space 1}   -3.85{col 48}{space 3}0.001{col 56}{space 4}-1.360251{col 69}{space 3}  -.41341
{txt}{space 5}partydum7 {c |}{col 16}{res}{space 2}-.6873122{col 28}{space 2} .1863913{col 39}{space 1}   -3.69{col 48}{space 3}0.001{col 56}{space 4}-1.070445{col 69}{space 3}-.3041795
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}  .312758{col 28}{space 2} .9580066{col 39}{space 1}    0.33{col 48}{space 3}0.747{col 56}{space 4}-1.656453{col 69}{space 3} 2.281969
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Iteration 0:   log pseudolikelihood = {res}-1087.5167
{txt}Iteration 1:   log pseudolikelihood = {res}-656.32222
{txt}Iteration 2:   log pseudolikelihood = {res}-621.78969
{txt}Iteration 3:   log pseudolikelihood = {res}-619.37374
{txt}Iteration 4:   log pseudolikelihood = {res} -619.2258
{txt}Iteration 5:   log pseudolikelihood = {res}-619.22291
{txt}Iteration 6:   log pseudolikelihood = {res}-619.22291

{txt}Logistic regression                               Number of obs   = {res}      1576
                                                  {txt}Wald chi2({res}16{txt})   = {res}    489.14
                                                  {txt}Prob > chi2     = {res}    0.0000
{txt}Log pseudolikelihood = {res}-619.22291                 {txt}Pseudo R2       = {res}    0.4306

                                 {txt}(Std. Err. adjusted for {res}27{txt} clusters in ccode)
{hline 13}{c TT}{hline 64}
             {c |}               Robust
prosanctions {c |}      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
{hline 13}{c +}{hline 64}
loggedmigr~s {c |}  {res}-.0682804   .0324393    -2.10   0.035    -.1318602   -.0047006
     {txt}lnremit {c |}  {res}-.1247929   .0477417    -2.61   0.009     -.218365   -.0312208
  {txt}population {c |}  {res} .0027084   .0042406     0.64   0.523    -.0056031      .01102
{txt}unemployment {c |}  {res}-.0513417   .0210269    -2.44   0.015    -.0925536   -.0101299
   {txt}gdpgrowth {c |}  {res} .0231568   .0699115     0.33   0.740    -.1138672    .1601808
    {txt}distance {c |}  {res} .0002659    .000115     2.31   0.021     .0000405    .0004913
         {txt}col {c |}  {res} .1988393   .1686382     1.18   0.238    -.1316855    .5293642
   {txt}libyabill {c |}  {res}  3.70046   .3224682    11.48   0.000     3.068434    4.332486
    {txt}iranbill {c |}  {res} 3.578553   .3849921     9.30   0.000     2.823982    4.333124
   {txt}partydum1 {c |}  {res} .5791662   1.167186     0.50   0.620    -1.708476    2.866808
   {txt}partydum2 {c |}  {res} 2.600446   .4704529     5.53   0.000     1.678375    3.522517
   {txt}partydum3 {c |}  {res} 2.654244   .4960584     5.35   0.000     1.681988    3.626501
   {txt}partydum4 {c |}  {res}-.1750564   .4983582    -0.35   0.725     -1.15182    .8017077
   {txt}partydum5 {c |}  {res} 2.738536   .5685064     4.82   0.000     1.624284    3.852788
   {txt}partydum6 {c |}  {res} 4.484114   .8509165     5.27   0.000     2.816348     6.15188
   {txt}partydum7 {c |}  {res} .6311591   .6341117     1.00   0.320    -.6116769    1.873995
       {txt}_cons {c |}  {res}-3.892857   .8036028    -4.84   0.000    -5.467889   -2.317824
{txt}{hline 13}{c BT}{hline 64}
{res}{txt}{hline 25}{c TT}{hline 52}
        Effect           {c |}  Mean           [90% Conf. Interval]
{hline 25}{c +}{hline 52}
{res}        ACME1            {c |} -.0093591     -.0207524     -.0008448
        ACME0            {c |} -.0093329     -.0207304     -.0008229
        Direct Effect 1  {c |} -.0250505     -.0444099     -.0047451
        Direct Effect 0  {c |} -.0250242     -.0443992     -.0047592
        Total Effect     {c |} -.0343834     -.0460615     -.0207783
{txt}{hline 25}{c BT}{hline 52}

{com}. 
. **TABLE A10
. //Model A59
. logit prosanctions ln_borderdetect, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1170.1153}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1170.1151}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1170.1151}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      1.43
{txt}{col 49}Prob > chi2{col 67}= {res}    0.2315
{txt}Log pseudolikelihood = {res}-1170.1151{txt}{col 49}Pseudo R2{col 67}= {res}    0.0018

{txt}{ralign 81:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}   prosanctions{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_borderdetect {c |}{col 17}{res}{space 2}-.0557325{col 29}{space 2} .0465767{col 40}{space 1}   -1.20{col 49}{space 3}0.231{col 57}{space 4}-.1470211{col 70}{space 3} .0355561
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .3307045{col 29}{space 2} .1336986{col 40}{space 1}    2.47{col 49}{space 3}0.013{col 57}{space 4} .0686601{col 70}{space 3} .5927488
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA59, title(Model A59)
{txt}
{com}. //Model A60
. logit prosanctions ln_borderdetect libyabill iranbill, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-834.99321}  
Iteration 2:{space 3}log pseudolikelihood = {res:-834.14348}  
Iteration 3:{space 3}log pseudolikelihood = {res:-834.14134}  
Iteration 4:{space 3}log pseudolikelihood = {res:-834.14134}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}Wald chi2({res}3{txt}){col 67}= {res}    139.30
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-834.14134{txt}{col 49}Pseudo R2{col 67}= {res}    0.2884

{txt}{ralign 81:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}   prosanctions{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_borderdetect {c |}{col 17}{res}{space 2}-.0261614{col 29}{space 2} .0561289{col 40}{space 1}   -0.47{col 49}{space 3}0.641{col 57}{space 4}-.1361719{col 70}{space 3} .0838492
{txt}{space 6}libyabill {c |}{col 17}{res}{space 2} 3.041368{col 29}{space 2} .2684291{col 40}{space 1}   11.33{col 49}{space 3}0.000{col 57}{space 4} 2.515257{col 70}{space 3}  3.56748
{txt}{space 7}iranbill {c |}{col 17}{res}{space 2} 3.175507{col 29}{space 2}  .347102{col 40}{space 1}    9.15{col 49}{space 3}0.000{col 57}{space 4} 2.495199{col 70}{space 3} 3.855814
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-1.831584{col 29}{space 2} .1596049{col 40}{space 1}  -11.48{col 49}{space 3}0.000{col 57}{space 4}-2.144404{col 70}{space 3}-1.518765
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA60, title(Model A60)
{txt}
{com}. //Model A61
. logit prosanctions ln_borderdetect libyabill iranbill i.ccode, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-797.95848}  
Iteration 2:{space 3}log pseudolikelihood = {res:-793.73804}  
Iteration 3:{space 3}log pseudolikelihood = {res:-793.71671}  
Iteration 4:{space 3}log pseudolikelihood = {res: -793.7167}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(2)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res} -793.7167{txt}{col 49}Pseudo R2{col 67}= {res}    0.3229

{txt}{ralign 81:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}   prosanctions{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_borderdetect {c |}{col 17}{res}{space 2} .0274082{col 29}{space 2} .2130906{col 40}{space 1}    0.13{col 49}{space 3}0.898{col 57}{space 4}-.3902417{col 70}{space 3} .4450581
{txt}{space 6}libyabill {c |}{col 17}{res}{space 2} 3.184312{col 29}{space 2} .2838018{col 40}{space 1}   11.22{col 49}{space 3}0.000{col 57}{space 4} 2.628071{col 70}{space 3} 3.740554
{txt}{space 7}iranbill {c |}{col 17}{res}{space 2} 3.285321{col 29}{space 2} .3756731{col 40}{space 1}    8.75{col 49}{space 3}0.000{col 57}{space 4} 2.549016{col 70}{space 3} 4.021627
{txt}{space 15} {c |}
{space 10}ccode {c |}
{space 11}205  {c |}{col 17}{res}{space 2}-1.400789{col 29}{space 2} .0451269{col 40}{space 1}  -31.04{col 49}{space 3}0.000{col 57}{space 4}-1.489236{col 70}{space 3}-1.312342
{txt}{space 11}210  {c |}{col 17}{res}{space 2}-.0542413{col 29}{space 2} .0286573{col 40}{space 1}   -1.89{col 49}{space 3}0.058{col 57}{space 4}-.1104086{col 70}{space 3} .0019261
{txt}{space 11}211  {c |}{col 17}{res}{space 2}-.5264051{col 29}{space 2} .0185471{col 40}{space 1}  -28.38{col 49}{space 3}0.000{col 57}{space 4}-.5627568{col 70}{space 3}-.4900533
{txt}{space 11}212  {c |}{col 17}{res}{space 2}-.3439734{col 29}{space 2} .0189239{col 40}{space 1}  -18.18{col 49}{space 3}0.000{col 57}{space 4}-.3810636{col 70}{space 3}-.3068832
{txt}{space 11}220  {c |}{col 17}{res}{space 2}-1.384671{col 29}{space 2} .0491307{col 40}{space 1}  -28.18{col 49}{space 3}0.000{col 57}{space 4}-1.480965{col 70}{space 3}-1.288377
{txt}{space 11}230  {c |}{col 17}{res}{space 2}-.6739512{col 29}{space 2} .2785417{col 40}{space 1}   -2.42{col 49}{space 3}0.016{col 57}{space 4}-1.219883{col 70}{space 3}-.1280196
{txt}{space 11}235  {c |}{col 17}{res}{space 2}-.0469288{col 29}{space 2} .0361156{col 40}{space 1}   -1.30{col 49}{space 3}0.194{col 57}{space 4}-.1177141{col 70}{space 3} .0238565
{txt}{space 11}255  {c |}{col 17}{res}{space 2}-.6212245{col 29}{space 2} .0110438{col 40}{space 1}  -56.25{col 49}{space 3}0.000{col 57}{space 4}-.6428698{col 70}{space 3}-.5995791
{txt}{space 11}290  {c |}{col 17}{res}{space 2} .1284816{col 29}{space 2} .2370115{col 40}{space 1}    0.54{col 49}{space 3}0.588{col 57}{space 4}-.3360523{col 70}{space 3} .5930155
{txt}{space 11}305  {c |}{col 17}{res}{space 2}-.8714952{col 29}{space 2} .0306301{col 40}{space 1}  -28.45{col 49}{space 3}0.000{col 57}{space 4}-.9315292{col 70}{space 3}-.8114613
{txt}{space 11}310  {c |}{col 17}{res}{space 2}-.4732571{col 29}{space 2}  .643547{col 40}{space 1}   -0.74{col 49}{space 3}0.462{col 57}{space 4}-1.734586{col 70}{space 3} .7880718
{txt}{space 11}316  {c |}{col 17}{res}{space 2} .2134258{col 29}{space 2} .0161784{col 40}{space 1}   13.19{col 49}{space 3}0.000{col 57}{space 4} .1817167{col 70}{space 3} .2451349
{txt}{space 11}317  {c |}{col 17}{res}{space 2}-.0763922{col 29}{space 2} .2580359{col 40}{space 1}   -0.30{col 49}{space 3}0.767{col 57}{space 4}-.5821332{col 70}{space 3} .4293489
{txt}{space 11}325  {c |}{col 17}{res}{space 2}-.9950021{col 29}{space 2} 1.065436{col 40}{space 1}   -0.93{col 49}{space 3}0.350{col 57}{space 4}-3.083217{col 70}{space 3} 1.093213
{txt}{space 11}338  {c |}{col 17}{res}{space 2}  .106096{col 29}{space 2}  1.09782{col 40}{space 1}    0.10{col 49}{space 3}0.923{col 57}{space 4}-2.045592{col 70}{space 3} 2.257784
{txt}{space 11}344  {c |}{col 17}{res}{space 2} 1.021436{col 29}{space 2} .6492679{col 40}{space 1}    1.57{col 49}{space 3}0.116{col 57}{space 4}-.2511053{col 70}{space 3} 2.293978
{txt}{space 11}349  {c |}{col 17}{res}{space 2}-.0520534{col 29}{space 2} .0285816{col 40}{space 1}   -1.82{col 49}{space 3}0.069{col 57}{space 4}-.1080724{col 70}{space 3} .0039656
{txt}{space 11}350  {c |}{col 17}{res}{space 2}-2.121073{col 29}{space 2} .7817542{col 40}{space 1}   -2.71{col 49}{space 3}0.007{col 57}{space 4}-3.653284{col 70}{space 3}-.5888634
{txt}{space 11}352  {c |}{col 17}{res}{space 2}-.8175605{col 29}{space 2} .0207518{col 40}{space 1}  -39.40{col 49}{space 3}0.000{col 57}{space 4}-.8582333{col 70}{space 3}-.7768877
{txt}{space 11}355  {c |}{col 17}{res}{space 2}-.2064273{col 29}{space 2} .6565616{col 40}{space 1}   -0.31{col 49}{space 3}0.753{col 57}{space 4}-1.493264{col 70}{space 3}  1.08041
{txt}{space 11}360  {c |}{col 17}{res}{space 2} .1655949{col 29}{space 2} .2524422{col 40}{space 1}    0.66{col 49}{space 3}0.512{col 57}{space 4}-.3291827{col 70}{space 3} .6603725
{txt}{space 11}366  {c |}{col 17}{res}{space 2}-.3473293{col 29}{space 2} .2172025{col 40}{space 1}   -1.60{col 49}{space 3}0.110{col 57}{space 4}-.7730385{col 70}{space 3} .0783799
{txt}{space 11}367  {c |}{col 17}{res}{space 2}-.4195679{col 29}{space 2} .2384796{col 40}{space 1}   -1.76{col 49}{space 3}0.079{col 57}{space 4}-.8869793{col 70}{space 3} .0478435
{txt}{space 11}368  {c |}{col 17}{res}{space 2}-.0600207{col 29}{space 2} .2394636{col 40}{space 1}   -0.25{col 49}{space 3}0.802{col 57}{space 4}-.5293607{col 70}{space 3} .4093193
{txt}{space 11}375  {c |}{col 17}{res}{space 2} .4516352{col 29}{space 2} .2839657{col 40}{space 1}    1.59{col 49}{space 3}0.112{col 57}{space 4}-.1049272{col 70}{space 3} 1.008198
{txt}{space 11}380  {c |}{col 17}{res}{space 2} -1.05516{col 29}{space 2} .0506749{col 40}{space 1}  -20.82{col 49}{space 3}0.000{col 57}{space 4}-1.154481{col 70}{space 3} -.955839
{txt}{space 11}390  {c |}{col 17}{res}{space 2}-.4272924{col 29}{space 2} .0006238{col 40}{space 1} -684.96{col 49}{space 3}0.000{col 57}{space 4}-.4285151{col 70}{space 3}-.4260698
{txt}{space 15} {c |}
{space 10}_cons {c |}{col 17}{res}{space 2}-1.440676{col 29}{space 2} .2264321{col 40}{space 1}   -6.36{col 49}{space 3}0.000{col 57}{space 4}-1.884475{col 70}{space 3}-.9968777
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA61, title(Model A61)
{txt}
{com}. //Model A62
. logit prosanctions ln_borderdetect libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res: -643.4713}  
Iteration 2:{space 3}log pseudolikelihood = {res: -636.1965}  
Iteration 3:{space 3}log pseudolikelihood = {res:-635.82149}  
Iteration 4:{space 3}log pseudolikelihood = {res:-635.81739}  
Iteration 5:{space 3}log pseudolikelihood = {res:-635.81738}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(9)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-635.81738{txt}{col 49}Pseudo R2{col 67}= {res}    0.4576

{txt}{ralign 81:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}   prosanctions{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_borderdetect {c |}{col 17}{res}{space 2} .1091344{col 29}{space 2} .1488999{col 40}{space 1}    0.73{col 49}{space 3}0.464{col 57}{space 4} -.182704{col 70}{space 3} .4009728
{txt}{space 6}libyabill {c |}{col 17}{res}{space 2} 3.885248{col 29}{space 2} .3062187{col 40}{space 1}   12.69{col 49}{space 3}0.000{col 57}{space 4}  3.28507{col 70}{space 3} 4.485425
{txt}{space 7}iranbill {c |}{col 17}{res}{space 2} 3.844283{col 29}{space 2} .4110851{col 40}{space 1}    9.35{col 49}{space 3}0.000{col 57}{space 4} 3.038571{col 70}{space 3} 4.649995
{txt}{space 15} {c |}
{space 10}ccode {c |}
{space 11}205  {c |}{col 17}{res}{space 2}-.8440501{col 29}{space 2} .0935921{col 40}{space 1}   -9.02{col 49}{space 3}0.000{col 57}{space 4}-1.027487{col 70}{space 3}-.6606128
{txt}{space 11}210  {c |}{col 17}{res}{space 2} .9459705{col 29}{space 2} .1673337{col 40}{space 1}    5.65{col 49}{space 3}0.000{col 57}{space 4} .6180024{col 70}{space 3} 1.273939
{txt}{space 11}211  {c |}{col 17}{res}{space 2}-.3688322{col 29}{space 2} .0602593{col 40}{space 1}   -6.12{col 49}{space 3}0.000{col 57}{space 4}-.4869382{col 70}{space 3}-.2507262
{txt}{space 11}212  {c |}{col 17}{res}{space 2} .0265177{col 29}{space 2} .0721592{col 40}{space 1}    0.37{col 49}{space 3}0.713{col 57}{space 4}-.1149118{col 70}{space 3} .1679471
{txt}{space 11}220  {c |}{col 17}{res}{space 2}-.7243737{col 29}{space 2}  .181368{col 40}{space 1}   -3.99{col 49}{space 3}0.000{col 57}{space 4}-1.079849{col 70}{space 3}-.3688989
{txt}{space 11}230  {c |}{col 17}{res}{space 2} -.432887{col 29}{space 2} .2369319{col 40}{space 1}   -1.83{col 49}{space 3}0.068{col 57}{space 4}-.8972651{col 70}{space 3}  .031491
{txt}{space 11}235  {c |}{col 17}{res}{space 2} .3880723{col 29}{space 2}  .148996{col 40}{space 1}    2.60{col 49}{space 3}0.009{col 57}{space 4} .0960455{col 70}{space 3}  .680099
{txt}{space 11}255  {c |}{col 17}{res}{space 2}-.0851097{col 29}{space 2} .0931894{col 40}{space 1}   -0.91{col 49}{space 3}0.361{col 57}{space 4}-.2677576{col 70}{space 3} .0975382
{txt}{space 11}290  {c |}{col 17}{res}{space 2}-.2096577{col 29}{space 2} .2259755{col 40}{space 1}   -0.93{col 49}{space 3}0.354{col 57}{space 4}-.6525616{col 70}{space 3} .2332461
{txt}{space 11}305  {c |}{col 17}{res}{space 2}-.1758013{col 29}{space 2} .2656698{col 40}{space 1}   -0.66{col 49}{space 3}0.508{col 57}{space 4}-.6965046{col 70}{space 3} .3449019
{txt}{space 11}310  {c |}{col 17}{res}{space 2}-.5400036{col 29}{space 2} .4984162{col 40}{space 1}   -1.08{col 49}{space 3}0.279{col 57}{space 4}-1.516881{col 70}{space 3} .4368741
{txt}{space 11}316  {c |}{col 17}{res}{space 2} .4371631{col 29}{space 2} .1683234{col 40}{space 1}    2.60{col 49}{space 3}0.009{col 57}{space 4} .1072553{col 70}{space 3} .7670708
{txt}{space 11}317  {c |}{col 17}{res}{space 2}-.3893635{col 29}{space 2} .2287087{col 40}{space 1}   -1.70{col 49}{space 3}0.089{col 57}{space 4}-.8376244{col 70}{space 3} .0588973
{txt}{space 11}325  {c |}{col 17}{res}{space 2}-1.139291{col 29}{space 2} .8208627{col 40}{space 1}   -1.39{col 49}{space 3}0.165{col 57}{space 4}-2.748152{col 70}{space 3} .4695701
{txt}{space 11}338  {c |}{col 17}{res}{space 2}-.5285079{col 29}{space 2} .7747149{col 40}{space 1}   -0.68{col 49}{space 3}0.495{col 57}{space 4}-2.046921{col 70}{space 3} .9899053
{txt}{space 11}344  {c |}{col 17}{res}{space 2} .6959553{col 29}{space 2}  .446528{col 40}{space 1}    1.56{col 49}{space 3}0.119{col 57}{space 4}-.1792235{col 70}{space 3} 1.571134
{txt}{space 11}349  {c |}{col 17}{res}{space 2}-.1209936{col 29}{space 2} .0699956{col 40}{space 1}   -1.73{col 49}{space 3}0.084{col 57}{space 4}-.2581825{col 70}{space 3} .0161953
{txt}{space 11}350  {c |}{col 17}{res}{space 2}-1.671775{col 29}{space 2} .7324119{col 40}{space 1}   -2.28{col 49}{space 3}0.022{col 57}{space 4}-3.107276{col 70}{space 3}-.2362742
{txt}{space 11}352  {c |}{col 17}{res}{space 2}-.0339942{col 29}{space 2} .2147657{col 40}{space 1}   -0.16{col 49}{space 3}0.874{col 57}{space 4}-.4549272{col 70}{space 3} .3869388
{txt}{space 11}355  {c |}{col 17}{res}{space 2}-.7094362{col 29}{space 2} .4828334{col 40}{space 1}   -1.47{col 49}{space 3}0.142{col 57}{space 4}-1.655772{col 70}{space 3}    .2369
{txt}{space 11}360  {c |}{col 17}{res}{space 2}-.0967251{col 29}{space 2} .1847695{col 40}{space 1}   -0.52{col 49}{space 3}0.601{col 57}{space 4}-.4588667{col 70}{space 3} .2654165
{txt}{space 11}366  {c |}{col 17}{res}{space 2}-.4113785{col 29}{space 2} .1714124{col 40}{space 1}   -2.40{col 49}{space 3}0.016{col 57}{space 4}-.7473405{col 70}{space 3}-.0754164
{txt}{space 11}367  {c |}{col 17}{res}{space 2}-.0495027{col 29}{space 2} .2343704{col 40}{space 1}   -0.21{col 49}{space 3}0.833{col 57}{space 4}-.5088603{col 70}{space 3} .4098548
{txt}{space 11}368  {c |}{col 17}{res}{space 2}-.1025971{col 29}{space 2} .2507187{col 40}{space 1}   -0.41{col 49}{space 3}0.682{col 57}{space 4}-.5939967{col 70}{space 3} .3888026
{txt}{space 11}375  {c |}{col 17}{res}{space 2} .8763631{col 29}{space 2} .2480835{col 40}{space 1}    3.53{col 49}{space 3}0.000{col 57}{space 4} .3901284{col 70}{space 3} 1.362598
{txt}{space 11}380  {c |}{col 17}{res}{space 2} -.559133{col 29}{space 2} .0924445{col 40}{space 1}   -6.05{col 49}{space 3}0.000{col 57}{space 4} -.740321{col 70}{space 3} -.377945
{txt}{space 11}390  {c |}{col 17}{res}{space 2} -.022879{col 29}{space 2} .0870581{col 40}{space 1}   -0.26{col 49}{space 3}0.793{col 57}{space 4}-.1935098{col 70}{space 3} .1477518
{txt}{space 15} {c |}
{space 10}party {c |}
{space 11}EPP  {c |}{col 17}{res}{space 2} 2.273697{col 29}{space 2}  .964416{col 40}{space 1}    2.36{col 49}{space 3}0.018{col 57}{space 4} .3834766{col 70}{space 3} 4.163918
{txt}{space 11}S&D  {c |}{col 17}{res}{space 2} 2.247767{col 29}{space 2}   .98067{col 40}{space 1}    2.29{col 49}{space 3}0.022{col 57}{space 4} .3256895{col 70}{space 3} 4.169845
{txt}{space 4}Greens/EFA  {c |}{col 17}{res}{space 2}-.7266759{col 29}{space 2} .9756743{col 40}{space 1}   -0.74{col 49}{space 3}0.456{col 57}{space 4}-2.638962{col 70}{space 3} 1.185611
{txt}{space 5}ALDE/ADLE  {c |}{col 17}{res}{space 2} 2.396822{col 29}{space 2} 1.007279{col 40}{space 1}    2.38{col 49}{space 3}0.017{col 57}{space 4} .4225907{col 70}{space 3} 4.371053
{txt}{space 11}ECR  {c |}{col 17}{res}{space 2} 4.151239{col 29}{space 2} 1.006403{col 40}{space 1}    4.12{col 49}{space 3}0.000{col 57}{space 4} 2.178725{col 70}{space 3} 6.123754
{txt}{space 10}EFDD  {c |}{col 17}{res}{space 2} .1527718{col 29}{space 2} .9721413{col 40}{space 1}    0.16{col 49}{space 3}0.875{col 57}{space 4} -1.75259{col 70}{space 3} 2.058134
{txt}{space 7}GUE-NGL  {c |}{col 17}{res}{space 2}-.9022384{col 29}{space 2} 1.244208{col 40}{space 1}   -0.73{col 49}{space 3}0.468{col 57}{space 4}-3.340841{col 70}{space 3} 1.536364
{txt}{space 15} {c |}
{space 10}_cons {c |}{col 17}{res}{space 2}-3.821161{col 29}{space 2} .9743336{col 40}{space 1}   -3.92{col 49}{space 3}0.000{col 57}{space 4} -5.73082{col 70}{space 3}-1.911502
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA62, title(Model A62)
{txt}
{com}. //Model A63
. logit prosanctions ln_borderdetect population unemployment gdpgrowth distance col libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-638.29345}  
Iteration 2:{space 3}log pseudolikelihood = {res:-630.47933}  
Iteration 3:{space 3}log pseudolikelihood = {res:-630.07508}  
Iteration 4:{space 3}log pseudolikelihood = {res:-630.07039}  
Iteration 5:{space 3}log pseudolikelihood = {res:-630.07039}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(14)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-630.07039{txt}{col 49}Pseudo R2{col 67}= {res}    0.4625

{txt}{ralign 81:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}   prosanctions{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
ln_borderdetect {c |}{col 17}{res}{space 2} .3834977{col 29}{space 2} .2301025{col 40}{space 1}    1.67{col 49}{space 3}0.096{col 57}{space 4}-.0674948{col 70}{space 3} .8344903
{txt}{space 5}population {c |}{col 17}{res}{space 2}-.0202924{col 29}{space 2} .1345806{col 40}{space 1}   -0.15{col 49}{space 3}0.880{col 57}{space 4}-.2840655{col 70}{space 3} .2434807
{txt}{space 3}unemployment {c |}{col 17}{res}{space 2} -.036408{col 29}{space 2} .0523853{col 40}{space 1}   -0.70{col 49}{space 3}0.487{col 57}{space 4}-.1390814{col 70}{space 3} .0662654
{txt}{space 6}gdpgrowth {c |}{col 17}{res}{space 2} .1513751{col 29}{space 2} .1078521{col 40}{space 1}    1.40{col 49}{space 3}0.160{col 57}{space 4}-.0600112{col 70}{space 3} .3627614
{txt}{space 7}distance {c |}{col 17}{res}{space 2}  .000775{col 29}{space 2}  .000393{col 40}{space 1}    1.97{col 49}{space 3}0.049{col 57}{space 4} 4.74e-06{col 70}{space 3} .0015453
{txt}{space 12}col {c |}{col 17}{res}{space 2}   .13695{col 29}{space 2} .1878085{col 40}{space 1}    0.73{col 49}{space 3}0.466{col 57}{space 4}-.2311479{col 70}{space 3} .5050478
{txt}{space 6}libyabill {c |}{col 17}{res}{space 2} 4.567682{col 29}{space 2}  .448312{col 40}{space 1}   10.19{col 49}{space 3}0.000{col 57}{space 4} 3.689007{col 70}{space 3} 5.446357
{txt}{space 7}iranbill {c |}{col 17}{res}{space 2} 3.353287{col 29}{space 2} .5278836{col 40}{space 1}    6.35{col 49}{space 3}0.000{col 57}{space 4} 2.318654{col 70}{space 3}  4.38792
{txt}{space 15} {c |}
{space 10}ccode {c |}
{space 11}205  {c |}{col 17}{res}{space 2}-2.256917{col 29}{space 2}  8.04335{col 40}{space 1}   -0.28{col 49}{space 3}0.779{col 57}{space 4}-18.02159{col 70}{space 3} 13.50776
{txt}{space 11}210  {c |}{col 17}{res}{space 2} .4786689{col 29}{space 2} 6.307422{col 40}{space 1}    0.08{col 49}{space 3}0.940{col 57}{space 4}-11.88365{col 70}{space 3} 12.84099
{txt}{space 11}211  {c |}{col 17}{res}{space 2}-.9372312{col 29}{space 2} 7.087506{col 40}{space 1}   -0.13{col 49}{space 3}0.895{col 57}{space 4}-14.82849{col 70}{space 3} 12.95402
{txt}{space 11}212  {c |}{col 17}{res}{space 2}-1.252719{col 29}{space 2} 8.715332{col 40}{space 1}   -0.14{col 49}{space 3}0.886{col 57}{space 4}-18.33446{col 70}{space 3} 15.82902
{txt}{space 11}220  {c |}{col 17}{res}{space 2} .0190632{col 29}{space 2} .3325299{col 40}{space 1}    0.06{col 49}{space 3}0.954{col 57}{space 4}-.6326835{col 70}{space 3} .6708098
{txt}{space 11}230  {c |}{col 17}{res}{space 2} .0240148{col 29}{space 2} 2.529463{col 40}{space 1}    0.01{col 49}{space 3}0.992{col 57}{space 4}-4.933641{col 70}{space 3}  4.98167
{txt}{space 11}235  {c |}{col 17}{res}{space 2}-.5144188{col 29}{space 2} 7.142944{col 40}{space 1}   -0.07{col 49}{space 3}0.943{col 57}{space 4}-14.51433{col 70}{space 3} 13.48549
{txt}{space 11}255  {c |}{col 17}{res}{space 2} .7567295{col 29}{space 2} 2.331456{col 40}{space 1}    0.32{col 49}{space 3}0.746{col 57}{space 4}-3.812841{col 70}{space 3}   5.3263
{txt}{space 11}290  {c |}{col 17}{res}{space 2} -.250571{col 29}{space 2} 3.606658{col 40}{space 1}   -0.07{col 49}{space 3}0.945{col 57}{space 4} -7.31949{col 70}{space 3} 6.818349
{txt}{space 11}305  {c |}{col 17}{res}{space 2}-.3137184{col 29}{space 2} 7.522355{col 40}{space 1}   -0.04{col 49}{space 3}0.967{col 57}{space 4}-15.05726{col 70}{space 3} 14.42983
{txt}{space 11}310  {c |}{col 17}{res}{space 2}-1.368194{col 29}{space 2} 7.293742{col 40}{space 1}   -0.19{col 49}{space 3}0.851{col 57}{space 4}-15.66367{col 70}{space 3} 12.92728
{txt}{space 11}316  {c |}{col 17}{res}{space 2} .2764137{col 29}{space 2} 7.182664{col 40}{space 1}    0.04{col 49}{space 3}0.969{col 57}{space 4}-13.80135{col 70}{space 3} 14.35418
{txt}{space 11}317  {c |}{col 17}{res}{space 2}-.8169934{col 29}{space 2} 8.104085{col 40}{space 1}   -0.10{col 49}{space 3}0.920{col 57}{space 4}-16.70071{col 70}{space 3} 15.06672
{txt}{space 11}325  {c |}{col 17}{res}{space 2}-1.152267{col 29}{space 2} .9081285{col 40}{space 1}   -1.27{col 49}{space 3}0.204{col 57}{space 4}-2.932167{col 70}{space 3} .6276317
{txt}{space 11}338  {c |}{col 17}{res}{space 2}-2.124924{col 29}{space 2} 8.450701{col 40}{space 1}   -0.25{col 49}{space 3}0.801{col 57}{space 4}-18.68799{col 70}{space 3} 14.43815
{txt}{space 11}344  {c |}{col 17}{res}{space 2} .4826579{col 29}{space 2} 7.978521{col 40}{space 1}    0.06{col 49}{space 3}0.952{col 57}{space 4}-15.15496{col 70}{space 3} 16.12027
{txt}{space 11}349  {c |}{col 17}{res}{space 2}-.1991175{col 29}{space 2} 8.412839{col 40}{space 1}   -0.02{col 49}{space 3}0.981{col 57}{space 4}-16.68798{col 70}{space 3} 16.28974
{txt}{space 11}350  {c |}{col 17}{res}{space 2}-1.186025{col 29}{space 2} 7.036682{col 40}{space 1}   -0.17{col 49}{space 3}0.866{col 57}{space 4}-14.97767{col 70}{space 3} 12.60562
{txt}{space 11}352  {c |}{col 17}{res}{space 2} 1.380815{col 29}{space 2} 8.510658{col 40}{space 1}    0.16{col 49}{space 3}0.871{col 57}{space 4}-15.29977{col 70}{space 3}  18.0614
{txt}{space 11}355  {c |}{col 17}{res}{space 2}-.9628056{col 29}{space 2} 7.598968{col 40}{space 1}   -0.13{col 49}{space 3}0.899{col 57}{space 4}-15.85651{col 70}{space 3}  13.9309
{txt}{space 11}360  {c |}{col 17}{res}{space 2} .2810035{col 29}{space 2} 5.921048{col 40}{space 1}    0.05{col 49}{space 3}0.962{col 57}{space 4}-11.32404{col 70}{space 3} 11.88604
{txt}{space 11}366  {c |}{col 17}{res}{space 2}-1.487109{col 29}{space 2} 8.427888{col 40}{space 1}   -0.18{col 49}{space 3}0.860{col 57}{space 4}-18.00547{col 70}{space 3} 15.03125
{txt}{space 11}367  {c |}{col 17}{res}{space 2}-.7845625{col 29}{space 2} 8.400976{col 40}{space 1}   -0.09{col 49}{space 3}0.926{col 57}{space 4}-17.25017{col 70}{space 3} 15.68105
{txt}{space 11}368  {c |}{col 17}{res}{space 2} -.818046{col 29}{space 2} 8.392246{col 40}{space 1}   -0.10{col 49}{space 3}0.922{col 57}{space 4}-17.26655{col 70}{space 3} 15.63045
{txt}{space 11}375  {c |}{col 17}{res}{space 2}-.1699402{col 29}{space 2} 7.823873{col 40}{space 1}   -0.02{col 49}{space 3}0.983{col 57}{space 4}-15.50445{col 70}{space 3} 15.16457
{txt}{space 11}380  {c |}{col 17}{res}{space 2}-1.427199{col 29}{space 2}  7.35648{col 40}{space 1}   -0.19{col 49}{space 3}0.846{col 57}{space 4}-15.84564{col 70}{space 3} 12.99124
{txt}{space 11}390  {c |}{col 17}{res}{space 2}-.6458327{col 29}{space 2} 7.781089{col 40}{space 1}   -0.08{col 49}{space 3}0.934{col 57}{space 4}-15.89649{col 70}{space 3} 14.60482
{txt}{space 15} {c |}
{space 10}party {c |}
{space 11}EPP  {c |}{col 17}{res}{space 2}  2.17839{col 29}{space 2} .9599806{col 40}{space 1}    2.27{col 49}{space 3}0.023{col 57}{space 4} .2968625{col 70}{space 3} 4.059917
{txt}{space 11}S&D  {c |}{col 17}{res}{space 2} 2.162231{col 29}{space 2} .9769795{col 40}{space 1}    2.21{col 49}{space 3}0.027{col 57}{space 4} .2473862{col 70}{space 3} 4.077075
{txt}{space 4}Greens/EFA  {c |}{col 17}{res}{space 2}-.8452993{col 29}{space 2} .9622237{col 40}{space 1}   -0.88{col 49}{space 3}0.380{col 57}{space 4}-2.731223{col 70}{space 3} 1.040624
{txt}{space 5}ALDE/ADLE  {c |}{col 17}{res}{space 2} 2.326178{col 29}{space 2} 1.009994{col 40}{space 1}    2.30{col 49}{space 3}0.021{col 57}{space 4} .3466258{col 70}{space 3}  4.30573
{txt}{space 11}ECR  {c |}{col 17}{res}{space 2} 4.043307{col 29}{space 2} .9858063{col 40}{space 1}    4.10{col 49}{space 3}0.000{col 57}{space 4} 2.111162{col 70}{space 3} 5.975452
{txt}{space 10}EFDD  {c |}{col 17}{res}{space 2} .1193237{col 29}{space 2} .9185589{col 40}{space 1}    0.13{col 49}{space 3}0.897{col 57}{space 4}-1.681019{col 70}{space 3} 1.919666
{txt}{space 7}GUE-NGL  {c |}{col 17}{res}{space 2}-.9919879{col 29}{space 2} 1.238234{col 40}{space 1}   -0.80{col 49}{space 3}0.423{col 57}{space 4}-3.418881{col 70}{space 3} 1.434906
{txt}{space 15} {c |}
{space 10}_cons {c |}{col 17}{res}{space 2}-5.379182{col 29}{space 2} 9.119279{col 40}{space 1}   -0.59{col 49}{space 3}0.555{col 57}{space 4}-23.25264{col 70}{space 3} 12.49428
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELA63, title(Model A63)
{txt}
{com}. 
. estout MODELA59 MODELA60 MODELA61 MODELA62 MODELA63, cells(b(star fmt(3)) se(par fmt(3))) starlevels($^{c -(}+{c )-}$ 0.10 $^{c -(}*{c )-}$ 0.05 $^{c -(}**{c )-}$ 0.01 $^{c -(}***{c )-}$ 0.001) stats(N N_g r2, fmt(0 0 3) label(Observations Countries R$^2$)) label,  using "modelsa59_a63.tex", replace style(tex)
{res}{txt}(note: file modelsa59_a63.tex not found)
(output written to {browse  `"modelsa59_a63.tex"'})

{com}. 
. **TABLE A11
. //See Do File title "Sanctions and Emigration"
. 
. **TABLE A12
. //Model I1
. logit prosanctions loggedmigrants RWPvoteshare migrantstockXrwpvote population unemployment gdpgrowth distance col libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-633.07979}  
Iteration 2:{space 3}log pseudolikelihood = {res: -625.1925}  
Iteration 3:{space 3}log pseudolikelihood = {res:-624.81309}  
Iteration 4:{space 3}log pseudolikelihood = {res:-624.80996}  
Iteration 5:{space 3}log pseudolikelihood = {res:-624.80996}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(16)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-624.80996{txt}{col 49}Pseudo R2{col 67}= {res}    0.4670

{txt}{ralign 86:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}        prosanctions{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}loggedmigrants {c |}{col 22}{res}{space 2}-.2168805{col 34}{space 2} .0908461{col 45}{space 1}   -2.39{col 54}{space 3}0.017{col 62}{space 4}-.3949355{col 75}{space 3}-.0388255
{txt}{space 8}RWPvoteshare {c |}{col 22}{res}{space 2} -.141189{col 34}{space 2} .0810038{col 45}{space 1}   -1.74{col 54}{space 3}0.081{col 62}{space 4}-.2999535{col 75}{space 3} .0175755
{txt}migrantstockXrwpvote {c |}{col 22}{res}{space 2} .0092132{col 34}{space 2} .0076527{col 45}{space 1}    1.20{col 54}{space 3}0.229{col 62}{space 4}-.0057858{col 75}{space 3} .0242121
{txt}{space 10}population {c |}{col 22}{res}{space 2} .3078699{col 34}{space 2} .2111039{col 45}{space 1}    1.46{col 54}{space 3}0.145{col 62}{space 4}-.1058861{col 75}{space 3}  .721626
{txt}{space 8}unemployment {c |}{col 22}{res}{space 2}-.0904624{col 34}{space 2} .0460123{col 45}{space 1}   -1.97{col 54}{space 3}0.049{col 62}{space 4}-.1806449{col 75}{space 3}-.0002799
{txt}{space 11}gdpgrowth {c |}{col 22}{res}{space 2} .0891094{col 34}{space 2} .0925497{col 45}{space 1}    0.96{col 54}{space 3}0.336{col 62}{space 4}-.0922847{col 75}{space 3} .2705036
{txt}{space 12}distance {c |}{col 22}{res}{space 2} .0006476{col 34}{space 2} .0002725{col 45}{space 1}    2.38{col 54}{space 3}0.017{col 62}{space 4} .0001135{col 75}{space 3} .0011817
{txt}{space 17}col {c |}{col 22}{res}{space 2} .3763169{col 34}{space 2} .3993038{col 45}{space 1}    0.94{col 54}{space 3}0.346{col 62}{space 4}-.4063041{col 75}{space 3} 1.158938
{txt}{space 11}libyabill {c |}{col 22}{res}{space 2} 4.107141{col 34}{space 2} .4065621{col 45}{space 1}   10.10{col 54}{space 3}0.000{col 62}{space 4} 3.310294{col 75}{space 3} 4.903988
{txt}{space 12}iranbill {c |}{col 22}{res}{space 2} 3.595265{col 34}{space 2} .4341434{col 45}{space 1}    8.28{col 54}{space 3}0.000{col 62}{space 4}  2.74436{col 75}{space 3} 4.446171
{txt}{space 20} {c |}
{space 15}ccode {c |}
{space 16}205  {c |}{col 22}{res}{space 2} 16.81538{col 34}{space 2} 12.40503{col 45}{space 1}    1.36{col 54}{space 3}0.175{col 62}{space 4}-7.498026{col 75}{space 3} 41.12879
{txt}{space 16}210  {c |}{col 22}{res}{space 2} 16.16319{col 34}{space 2} 9.800541{col 45}{space 1}    1.65{col 54}{space 3}0.099{col 62}{space 4}-3.045513{col 75}{space 3}  35.3719
{txt}{space 16}211  {c |}{col 22}{res}{space 2} 15.71811{col 34}{space 2} 10.69075{col 45}{space 1}    1.47{col 54}{space 3}0.141{col 62}{space 4}-5.235373{col 75}{space 3} 36.67159
{txt}{space 16}212  {c |}{col 22}{res}{space 2} 17.68886{col 34}{space 2} 12.89103{col 45}{space 1}    1.37{col 54}{space 3}0.170{col 62}{space 4}  -7.5771{col 75}{space 3} 42.95481
{txt}{space 16}220  {c |}{col 22}{res}{space 2}-.2504041{col 34}{space 2} .8210067{col 45}{space 1}   -0.30{col 54}{space 3}0.760{col 62}{space 4}-1.859548{col 75}{space 3} 1.358739
{txt}{space 16}230  {c |}{col 22}{res}{space 2} 5.801445{col 34}{space 2}  3.67917{col 45}{space 1}    1.58{col 54}{space 3}0.115{col 62}{space 4}-1.409596{col 75}{space 3} 13.01249
{txt}{space 16}235  {c |}{col 22}{res}{space 2} 15.94195{col 34}{space 2} 10.86367{col 45}{space 1}    1.47{col 54}{space 3}0.142{col 62}{space 4}-5.350458{col 75}{space 3} 37.23435
{txt}{space 16}255  {c |}{col 22}{res}{space 2}-4.947054{col 34}{space 2} 3.671455{col 45}{space 1}   -1.35{col 54}{space 3}0.178{col 62}{space 4}-12.14297{col 75}{space 3} 2.248866
{txt}{space 16}290  {c |}{col 22}{res}{space 2} 7.733604{col 34}{space 2} 5.301795{col 45}{space 1}    1.46{col 54}{space 3}0.145{col 62}{space 4}-2.657723{col 75}{space 3} 18.12493
{txt}{space 16}305  {c |}{col 22}{res}{space 2} 18.77079{col 34}{space 2} 11.50742{col 45}{space 1}    1.63{col 54}{space 3}0.103{col 62}{space 4}-3.783341{col 75}{space 3} 41.32491
{txt}{space 16}310  {c |}{col 22}{res}{space 2} 18.20871{col 34}{space 2} 11.30183{col 45}{space 1}    1.61{col 54}{space 3}0.107{col 62}{space 4} -3.94246{col 75}{space 3} 40.35988
{txt}{space 16}316  {c |}{col 22}{res}{space 2}  17.2974{col 34}{space 2} 10.91462{col 45}{space 1}    1.58{col 54}{space 3}0.113{col 62}{space 4}-4.094865{col 75}{space 3} 38.68966
{txt}{space 16}317  {c |}{col 22}{res}{space 2} 18.47325{col 34}{space 2}  12.1633{col 45}{space 1}    1.52{col 54}{space 3}0.129{col 62}{space 4}-5.366386{col 75}{space 3} 42.31288
{txt}{space 16}325  {c |}{col 22}{res}{space 2}  2.06169{col 34}{space 2} .9057003{col 45}{space 1}    2.28{col 54}{space 3}0.023{col 62}{space 4}   .28655{col 75}{space 3}  3.83683
{txt}{space 16}338  {c |}{col 22}{res}{space 2} 19.64439{col 34}{space 2} 13.32674{col 45}{space 1}    1.47{col 54}{space 3}0.140{col 62}{space 4}-6.475548{col 75}{space 3} 45.76433
{txt}{space 16}344  {c |}{col 22}{res}{space 2} 20.12215{col 34}{space 2} 12.05375{col 45}{space 1}    1.67{col 54}{space 3}0.095{col 62}{space 4}-3.502769{col 75}{space 3} 43.74707
{txt}{space 16}349  {c |}{col 22}{res}{space 2} 18.90566{col 34}{space 2} 12.66114{col 45}{space 1}    1.49{col 54}{space 3}0.135{col 62}{space 4}-5.909708{col 75}{space 3} 43.72103
{txt}{space 16}350  {c |}{col 22}{res}{space 2} 18.47229{col 34}{space 2} 11.25597{col 45}{space 1}    1.64{col 54}{space 3}0.101{col 62}{space 4}-3.589003{col 75}{space 3} 40.53359
{txt}{space 16}352  {c |}{col 22}{res}{space 2}  20.9915{col 34}{space 2} 13.10243{col 45}{space 1}    1.60{col 54}{space 3}0.109{col 62}{space 4}-4.688783{col 75}{space 3} 46.67179
{txt}{space 16}355  {c |}{col 22}{res}{space 2} 18.78309{col 34}{space 2} 11.76971{col 45}{space 1}    1.60{col 54}{space 3}0.111{col 62}{space 4}-4.285125{col 75}{space 3}  41.8513
{txt}{space 16}360  {c |}{col 22}{res}{space 2}  14.2499{col 34}{space 2} 9.083377{col 45}{space 1}    1.57{col 54}{space 3}0.117{col 62}{space 4}-3.553189{col 75}{space 3} 32.05299
{txt}{space 16}366  {c |}{col 22}{res}{space 2} 17.37753{col 34}{space 2} 12.57738{col 45}{space 1}    1.38{col 54}{space 3}0.167{col 62}{space 4} -7.27368{col 75}{space 3} 42.02873
{txt}{space 16}367  {c |}{col 22}{res}{space 2} 20.18572{col 34}{space 2} 12.56062{col 45}{space 1}    1.61{col 54}{space 3}0.108{col 62}{space 4}-4.432651{col 75}{space 3} 44.80409
{txt}{space 16}368  {c |}{col 22}{res}{space 2}  18.1702{col 34}{space 2} 12.56176{col 45}{space 1}    1.45{col 54}{space 3}0.148{col 62}{space 4}-6.450407{col 75}{space 3}  42.7908
{txt}{space 16}375  {c |}{col 22}{res}{space 2} 19.39368{col 34}{space 2} 12.05961{col 45}{space 1}    1.61{col 54}{space 3}0.108{col 62}{space 4}-4.242713{col 75}{space 3} 43.03007
{txt}{space 16}380  {c |}{col 22}{res}{space 2} 16.75733{col 34}{space 2} 11.45035{col 45}{space 1}    1.46{col 54}{space 3}0.143{col 62}{space 4}-5.684949{col 75}{space 3} 39.19961
{txt}{space 16}390  {c |}{col 22}{res}{space 2}  18.6654{col 34}{space 2} 12.02534{col 45}{space 1}    1.55{col 54}{space 3}0.121{col 62}{space 4} -4.90383{col 75}{space 3} 42.23463
{txt}{space 20} {c |}
{space 15}party {c |}
{space 16}EPP  {c |}{col 22}{res}{space 2} 2.173829{col 34}{space 2} .9523499{col 45}{space 1}    2.28{col 54}{space 3}0.022{col 62}{space 4} .3072577{col 75}{space 3} 4.040401
{txt}{space 16}S&D  {c |}{col 22}{res}{space 2} 2.176889{col 34}{space 2} .9712693{col 45}{space 1}    2.24{col 54}{space 3}0.025{col 62}{space 4} .2732359{col 75}{space 3} 4.080541
{txt}{space 9}Greens/EFA  {c |}{col 22}{res}{space 2}-.7860421{col 34}{space 2} .9714423{col 45}{space 1}   -0.81{col 54}{space 3}0.418{col 62}{space 4}-2.690034{col 75}{space 3}  1.11795
{txt}{space 10}ALDE/ADLE  {c |}{col 22}{res}{space 2} 2.331241{col 34}{space 2} 1.002389{col 45}{space 1}    2.33{col 54}{space 3}0.020{col 62}{space 4} .3665956{col 75}{space 3} 4.295886
{txt}{space 16}ECR  {c |}{col 22}{res}{space 2} 3.996859{col 34}{space 2} .9976763{col 45}{space 1}    4.01{col 54}{space 3}0.000{col 62}{space 4}  2.04145{col 75}{space 3} 5.952269
{txt}{space 15}EFDD  {c |}{col 22}{res}{space 2} .0987968{col 34}{space 2} .9309586{col 45}{space 1}    0.11{col 54}{space 3}0.915{col 62}{space 4}-1.725849{col 75}{space 3} 1.923442
{txt}{space 12}GUE-NGL  {c |}{col 22}{res}{space 2}-.9624796{col 34}{space 2} 1.241877{col 45}{space 1}   -0.78{col 54}{space 3}0.438{col 62}{space 4}-3.396514{col 75}{space 3} 1.471555
{txt}{space 20} {c |}
{space 15}_cons {c |}{col 22}{res}{space 2}-23.08674{col 34}{space 2} 12.99909{col 45}{space 1}   -1.78{col 54}{space 3}0.076{col 62}{space 4} -48.5645{col 75}{space 3} 2.391012
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELI1, title(Model I1)
{txt}
{com}. //Model I2
. logit prosanctions c.loggedmigrants##c.immigrantpolicy population unemployment gdpgrowth distance col libyabill iranbill i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-647.95222}  
Iteration 2:{space 3}log pseudolikelihood = {res:-641.20082}  
Iteration 3:{space 3}log pseudolikelihood = {res:-640.84529}  
Iteration 4:{space 3}log pseudolikelihood = {res:-640.84275}  
Iteration 5:{space 3}log pseudolikelihood = {res:-640.84275}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}Wald chi2({res}17{txt}){col 67}= {res}    669.56
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-640.84275{txt}{col 49}Pseudo R2{col 67}= {res}    0.4533

{txt}{ralign 100:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                      prosanctions{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}loggedmigrants {c |}{col 36}{res}{space 2} -1.19894{col 48}{space 2} .4594521{col 59}{space 1}   -2.61{col 68}{space 3}0.009{col 76}{space 4} -2.09945{col 89}{space 3} -.298431
{txt}{space 19}immigrantpolicy {c |}{col 36}{res}{space 2}-3.151753{col 48}{space 2} 1.454847{col 59}{space 1}   -2.17{col 68}{space 3}0.030{col 76}{space 4}-6.003201{col 89}{space 3}-.3003041
{txt}{space 34} {c |}
c.loggedmigrants#c.immigrantpolicy {c |}{col 36}{res}{space 2} .4518999{col 48}{space 2} .1857322{col 59}{space 1}    2.43{col 68}{space 3}0.015{col 76}{space 4} .0878714{col 89}{space 3} .8159283
{txt}{space 34} {c |}
{space 24}population {c |}{col 36}{res}{space 2}-.0040481{col 48}{space 2} .0041781{col 59}{space 1}   -0.97{col 68}{space 3}0.333{col 76}{space 4}-.0122371{col 89}{space 3} .0041409
{txt}{space 22}unemployment {c |}{col 36}{res}{space 2}-.0524404{col 48}{space 2}   .02159{col 59}{space 1}   -2.43{col 68}{space 3}0.015{col 76}{space 4} -.094756{col 89}{space 3}-.0101249
{txt}{space 25}gdpgrowth {c |}{col 36}{res}{space 2}-.0122143{col 48}{space 2} .0564279{col 59}{space 1}   -0.22{col 68}{space 3}0.829{col 76}{space 4}-.1228109{col 89}{space 3} .0983822
{txt}{space 26}distance {c |}{col 36}{res}{space 2} .0002216{col 48}{space 2} .0001128{col 59}{space 1}    1.96{col 68}{space 3}0.049{col 76}{space 4} 5.40e-07{col 89}{space 3} .0004427
{txt}{space 31}col {c |}{col 36}{res}{space 2} .2549373{col 48}{space 2} .1839788{col 59}{space 1}    1.39{col 68}{space 3}0.166{col 76}{space 4}-.1056545{col 89}{space 3} .6155291
{txt}{space 25}libyabill {c |}{col 36}{res}{space 2} 3.780652{col 48}{space 2} .2956795{col 59}{space 1}   12.79{col 68}{space 3}0.000{col 76}{space 4} 3.201131{col 89}{space 3} 4.360174
{txt}{space 26}iranbill {c |}{col 36}{res}{space 2} 3.653256{col 48}{space 2} .3774624{col 59}{space 1}    9.68{col 68}{space 3}0.000{col 76}{space 4} 2.913444{col 89}{space 3} 4.393069
{txt}{space 34} {c |}
{space 29}party {c |}
{space 30}EPP  {c |}{col 36}{res}{space 2} 2.236546{col 48}{space 2} .9117208{col 59}{space 1}    2.45{col 68}{space 3}0.014{col 76}{space 4} .4496064{col 89}{space 3} 4.023486
{txt}{space 30}S&D  {c |}{col 36}{res}{space 2} 2.244026{col 48}{space 2} .9290351{col 59}{space 1}    2.42{col 68}{space 3}0.016{col 76}{space 4} .4231507{col 89}{space 3} 4.064901
{txt}{space 23}Greens/EFA  {c |}{col 36}{res}{space 2}-.8022697{col 48}{space 2} .9139186{col 59}{space 1}   -0.88{col 68}{space 3}0.380{col 76}{space 4}-2.593517{col 89}{space 3} .9889778
{txt}{space 24}ALDE/ADLE  {c |}{col 36}{res}{space 2} 2.311491{col 48}{space 2} .9370302{col 59}{space 1}    2.47{col 68}{space 3}0.014{col 76}{space 4} .4749452{col 89}{space 3} 4.148036
{txt}{space 30}ECR  {c |}{col 36}{res}{space 2} 4.091634{col 48}{space 2}  1.00888{col 59}{space 1}    4.06{col 68}{space 3}0.000{col 76}{space 4} 2.114266{col 89}{space 3} 6.069002
{txt}{space 29}EFDD  {c |}{col 36}{res}{space 2} .2655569{col 48}{space 2} .9096679{col 59}{space 1}    0.29{col 68}{space 3}0.770{col 76}{space 4}-1.517359{col 89}{space 3} 2.048473
{txt}{space 26}GUE-NGL  {c |}{col 36}{res}{space 2}-.8325226{col 48}{space 2} 1.128308{col 59}{space 1}   -0.74{col 68}{space 3}0.461{col 76}{space 4}-3.043965{col 89}{space 3}  1.37892
{txt}{space 34} {c |}
{space 29}_cons {c |}{col 36}{res}{space 2} 4.536989{col 48}{space 2} 4.018396{col 59}{space 1}    1.13{col 68}{space 3}0.259{col 76}{space 4}-3.338923{col 89}{space 3}  12.4129
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELI2, title(Model I2)
{txt}
{com}. //Model I3
. logit prosanctions loggedmigrants sstax migrantstockXsstax population unemployment gdpgrowth distance col libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1045.4561}  
Iteration 1:{space 3}log pseudolikelihood = {res: -594.0631}  
Iteration 2:{space 3}log pseudolikelihood = {res:-588.02306}  
Iteration 3:{space 3}log pseudolikelihood = {res:-587.66227}  
Iteration 4:{space 3}log pseudolikelihood = {res: -587.6598}  
Iteration 5:{space 3}log pseudolikelihood = {res: -587.6598}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,522
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(16)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res} -587.6598{txt}{col 49}Pseudo R2{col 67}= {res}    0.4379

{txt}{ralign 84:(Std. Err. adjusted for {res:22} clusters in ccode)}
{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}      prosanctions{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}loggedmigrants {c |}{col 20}{res}{space 2}-.1372283{col 32}{space 2} .1850521{col 43}{space 1}   -0.74{col 52}{space 3}0.458{col 60}{space 4}-.4999237{col 73}{space 3} .2254671
{txt}{space 13}sstax {c |}{col 20}{res}{space 2} .2101252{col 32}{space 2} .5314894{col 43}{space 1}    0.40{col 52}{space 3}0.693{col 60}{space 4}-.8315749{col 73}{space 3} 1.251825
{txt}migrantstockXsstax {c |}{col 20}{res}{space 2}-.0066223{col 32}{space 2}  .013974{col 43}{space 1}   -0.47{col 52}{space 3}0.636{col 60}{space 4} -.034011{col 73}{space 3} .0207663
{txt}{space 8}population {c |}{col 20}{res}{space 2} .3655203{col 32}{space 2} .2116206{col 43}{space 1}    1.73{col 52}{space 3}0.084{col 60}{space 4}-.0492486{col 73}{space 3} .7802891
{txt}{space 6}unemployment {c |}{col 20}{res}{space 2}-.0604386{col 32}{space 2} .0529217{col 43}{space 1}   -1.14{col 52}{space 3}0.253{col 60}{space 4}-.1641632{col 73}{space 3}  .043286
{txt}{space 9}gdpgrowth {c |}{col 20}{res}{space 2} .0294592{col 32}{space 2} .1108856{col 43}{space 1}    0.27{col 52}{space 3}0.790{col 60}{space 4}-.1878725{col 73}{space 3} .2467909
{txt}{space 10}distance {c |}{col 20}{res}{space 2} .0004441{col 32}{space 2} .0003118{col 43}{space 1}    1.42{col 52}{space 3}0.154{col 60}{space 4}-.0001671{col 73}{space 3} .0010553
{txt}{space 15}col {c |}{col 20}{res}{space 2} .8334898{col 32}{space 2} .4600085{col 43}{space 1}    1.81{col 52}{space 3}0.070{col 60}{space 4}-.0681103{col 73}{space 3}  1.73509
{txt}{space 9}libyabill {c |}{col 20}{res}{space 2} 3.442845{col 32}{space 2} .3270934{col 43}{space 1}   10.53{col 52}{space 3}0.000{col 60}{space 4} 2.801754{col 73}{space 3} 4.083936
{txt}{space 10}iranbill {c |}{col 20}{res}{space 2}  3.52423{col 32}{space 2} .3492286{col 43}{space 1}   10.09{col 52}{space 3}0.000{col 60}{space 4} 2.839754{col 73}{space 3} 4.208705
{txt}{space 18} {c |}
{space 13}ccode {c |}
{space 14}205  {c |}{col 20}{res}{space 2} 20.54369{col 32}{space 2} 12.49193{col 43}{space 1}    1.64{col 52}{space 3}0.100{col 60}{space 4}-3.940036{col 73}{space 3} 45.02741
{txt}{space 14}210  {c |}{col 20}{res}{space 2} 16.79263{col 32}{space 2} 9.768845{col 43}{space 1}    1.72{col 52}{space 3}0.086{col 60}{space 4}-2.353951{col 73}{space 3} 35.93922
{txt}{space 14}211  {c |}{col 20}{res}{space 2} 16.82902{col 32}{space 2} 10.75387{col 43}{space 1}    1.56{col 52}{space 3}0.118{col 60}{space 4}-4.248177{col 73}{space 3} 37.90621
{txt}{space 14}212  {c |}{col 20}{res}{space 2} 20.72436{col 32}{space 2} 12.78682{col 43}{space 1}    1.62{col 52}{space 3}0.105{col 60}{space 4} -4.33735{col 73}{space 3} 45.78606
{txt}{space 14}220  {c |}{col 20}{res}{space 2}-3.189541{col 32}{space 2} 6.214521{col 43}{space 1}   -0.51{col 52}{space 3}0.608{col 60}{space 4}-15.36978{col 73}{space 3} 8.990696
{txt}{space 14}230  {c |}{col 20}{res}{space 2} 5.357662{col 32}{space 2} 4.200361{col 43}{space 1}    1.28{col 52}{space 3}0.202{col 60}{space 4}-2.874894{col 73}{space 3} 13.59022
{txt}{space 14}235  {c |}{col 20}{res}{space 2} 18.51246{col 32}{space 2} 10.67701{col 43}{space 1}    1.73{col 52}{space 3}0.083{col 60}{space 4}-2.414096{col 73}{space 3} 39.43901
{txt}{space 14}255  {c |}{col 20}{res}{space 2}-7.337947{col 32}{space 2} 5.881979{col 43}{space 1}   -1.25{col 52}{space 3}0.212{col 60}{space 4}-18.86641{col 73}{space 3}  4.19052
{txt}{space 14}290  {c |}{col 20}{res}{space 2} 8.135533{col 32}{space 2} 5.646092{col 43}{space 1}    1.44{col 52}{space 3}0.150{col 60}{space 4}-2.930604{col 73}{space 3} 19.20167
{txt}{space 14}305  {c |}{col 20}{res}{space 2} 18.31717{col 32}{space 2} 11.72719{col 43}{space 1}    1.56{col 52}{space 3}0.118{col 60}{space 4}-4.667691{col 73}{space 3} 41.30204
{txt}{space 14}310  {c |}{col 20}{res}{space 2} 18.59242{col 32}{space 2} 11.24651{col 43}{space 1}    1.65{col 52}{space 3}0.098{col 60}{space 4}-3.450337{col 73}{space 3} 40.63517
{txt}{space 14}316  {c |}{col 20}{res}{space 2} 18.18309{col 32}{space 2} 11.04628{col 43}{space 1}    1.65{col 52}{space 3}0.100{col 60}{space 4}-3.467224{col 73}{space 3} 39.83341
{txt}{space 14}317  {c |}{col 20}{res}{space 2}  19.7709{col 32}{space 2} 12.20862{col 43}{space 1}    1.62{col 52}{space 3}0.105{col 60}{space 4}-4.157549{col 73}{space 3} 43.69936
{txt}{space 14}325  {c |}{col 20}{res}{space 2} .3203899{col 32}{space 2}  3.42953{col 43}{space 1}    0.09{col 52}{space 3}0.926{col 60}{space 4}-6.401365{col 73}{space 3} 7.042145
{txt}{space 14}349  {c |}{col 20}{res}{space 2} 20.45088{col 32}{space 2} 12.72642{col 43}{space 1}    1.61{col 52}{space 3}0.108{col 60}{space 4} -4.49244{col 73}{space 3} 45.39421
{txt}{space 14}350  {c |}{col 20}{res}{space 2} 18.87011{col 32}{space 2} 11.17173{col 43}{space 1}    1.69{col 52}{space 3}0.091{col 60}{space 4}-3.026084{col 73}{space 3} 40.76631
{txt}{space 14}366  {c |}{col 20}{res}{space 2} 20.05744{col 32}{space 2} 12.49634{col 43}{space 1}    1.61{col 52}{space 3}0.108{col 60}{space 4}-4.434939{col 73}{space 3} 44.54981
{txt}{space 14}367  {c |}{col 20}{res}{space 2} 21.66283{col 32}{space 2}  12.6015{col 43}{space 1}    1.72{col 52}{space 3}0.086{col 60}{space 4}-3.035658{col 73}{space 3} 46.36132
{txt}{space 14}375  {c |}{col 20}{res}{space 2} 20.80545{col 32}{space 2} 11.96139{col 43}{space 1}    1.74{col 52}{space 3}0.082{col 60}{space 4}-2.638442{col 73}{space 3} 44.24935
{txt}{space 14}380  {c |}{col 20}{res}{space 2} 18.14017{col 32}{space 2} 11.40282{col 43}{space 1}    1.59{col 52}{space 3}0.112{col 60}{space 4}-4.208958{col 73}{space 3} 40.48929
{txt}{space 14}390  {c |}{col 20}{res}{space 2} 21.92004{col 32}{space 2} 13.02234{col 43}{space 1}    1.68{col 52}{space 3}0.092{col 60}{space 4}-3.603269{col 73}{space 3} 47.44336
{txt}{space 18} {c |}
{space 13}party {c |}
{space 14}EPP  {c |}{col 20}{res}{space 2} 2.281553{col 32}{space 2} .9947132{col 43}{space 1}    2.29{col 52}{space 3}0.022{col 60}{space 4}  .331951{col 73}{space 3} 4.231155
{txt}{space 14}S&D  {c |}{col 20}{res}{space 2} 2.271568{col 32}{space 2} 1.005606{col 43}{space 1}    2.26{col 52}{space 3}0.024{col 60}{space 4} .3006172{col 73}{space 3} 4.242519
{txt}{space 7}Greens/EFA  {c |}{col 20}{res}{space 2}-.5984493{col 32}{space 2} .9952535{col 43}{space 1}   -0.60{col 52}{space 3}0.548{col 60}{space 4} -2.54911{col 73}{space 3} 1.352212
{txt}{space 8}ALDE/ADLE  {c |}{col 20}{res}{space 2} 2.453074{col 32}{space 2} 1.058145{col 43}{space 1}    2.32{col 52}{space 3}0.020{col 60}{space 4} .3791479{col 73}{space 3} 4.526999
{txt}{space 14}ECR  {c |}{col 20}{res}{space 2}  4.15231{col 32}{space 2}  1.02269{col 43}{space 1}    4.06{col 52}{space 3}0.000{col 60}{space 4} 2.147874{col 73}{space 3} 6.156746
{txt}{space 13}EFDD  {c |}{col 20}{res}{space 2} .1527955{col 32}{space 2} .9451158{col 43}{space 1}    0.16{col 52}{space 3}0.872{col 60}{space 4}-1.699597{col 73}{space 3} 2.005188
{txt}{space 10}GUE-NGL  {c |}{col 20}{res}{space 2}-.8131178{col 32}{space 2} 1.279723{col 43}{space 1}   -0.64{col 52}{space 3}0.525{col 60}{space 4}-3.321328{col 73}{space 3} 1.695092
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2}-27.65076{col 32}{space 2} 14.33891{col 43}{space 1}   -1.93{col 52}{space 3}0.054{col 60}{space 4} -55.7545{col 73}{space 3} .4529844
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELI3, title(Model I3)
{txt}
{com}. //Model I4
. logit prosanctions migrantshare RWPvoteshare migrantshareXrwpvote population unemployment gdpgrowth distance col libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-629.23773}  
Iteration 2:{space 3}log pseudolikelihood = {res:-620.94416}  
Iteration 3:{space 3}log pseudolikelihood = {res:-620.54817}  
Iteration 4:{space 3}log pseudolikelihood = {res:-620.54483}  
Iteration 5:{space 3}log pseudolikelihood = {res:-620.54483}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(16)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-620.54483{txt}{col 49}Pseudo R2{col 67}= {res}    0.4706

{txt}{ralign 86:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}        prosanctions{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}migrantshare {c |}{col 22}{res}{space 2}-3.653682{col 34}{space 2} 1.299652{col 45}{space 1}   -2.81{col 54}{space 3}0.005{col 62}{space 4}-6.200954{col 75}{space 3} -1.10641
{txt}{space 8}RWPvoteshare {c |}{col 22}{res}{space 2}-.1051614{col 34}{space 2} .0499966{col 45}{space 1}   -2.10{col 54}{space 3}0.035{col 62}{space 4}-.2031529{col 75}{space 3}  -.00717
{txt}migrantshareXrwpvote {c |}{col 22}{res}{space 2}-.3188163{col 34}{space 2} .1326478{col 45}{space 1}   -2.40{col 54}{space 3}0.016{col 62}{space 4}-.5788013{col 75}{space 3}-.0588314
{txt}{space 10}population {c |}{col 22}{res}{space 2} -.030131{col 34}{space 2} .1330416{col 45}{space 1}   -0.23{col 54}{space 3}0.821{col 62}{space 4}-.2908878{col 75}{space 3} .2306258
{txt}{space 8}unemployment {c |}{col 22}{res}{space 2}-.1019808{col 34}{space 2} .0414302{col 45}{space 1}   -2.46{col 54}{space 3}0.014{col 62}{space 4}-.1831824{col 75}{space 3}-.0207792
{txt}{space 11}gdpgrowth {c |}{col 22}{res}{space 2} .1153899{col 34}{space 2} .0861472{col 45}{space 1}    1.34{col 54}{space 3}0.180{col 62}{space 4}-.0534554{col 75}{space 3} .2842353
{txt}{space 12}distance {c |}{col 22}{res}{space 2}-.0000734{col 34}{space 2} .0002112{col 45}{space 1}   -0.35{col 54}{space 3}0.728{col 62}{space 4}-.0004873{col 75}{space 3} .0003405
{txt}{space 17}col {c |}{col 22}{res}{space 2} .0504353{col 34}{space 2} .3116159{col 45}{space 1}    0.16{col 54}{space 3}0.871{col 62}{space 4}-.5603206{col 75}{space 3} .6611912
{txt}{space 11}libyabill {c |}{col 22}{res}{space 2} 4.036027{col 34}{space 2} .4245499{col 45}{space 1}    9.51{col 54}{space 3}0.000{col 62}{space 4} 3.203924{col 75}{space 3} 4.868129
{txt}{space 12}iranbill {c |}{col 22}{res}{space 2} 4.763274{col 34}{space 2} .4955364{col 45}{space 1}    9.61{col 54}{space 3}0.000{col 62}{space 4} 3.792041{col 75}{space 3} 5.734508
{txt}{space 20} {c |}
{space 15}ccode {c |}
{space 16}205  {c |}{col 22}{res}{space 2}-2.380988{col 34}{space 2} 7.923877{col 45}{space 1}   -0.30{col 54}{space 3}0.764{col 62}{space 4} -17.9115{col 75}{space 3} 13.14952
{txt}{space 16}210  {c |}{col 22}{res}{space 2} 1.222531{col 34}{space 2} 6.318753{col 45}{space 1}    0.19{col 54}{space 3}0.847{col 62}{space 4}  -11.162{col 75}{space 3} 13.60706
{txt}{space 16}211  {c |}{col 22}{res}{space 2}-1.237222{col 34}{space 2} 6.965067{col 45}{space 1}   -0.18{col 54}{space 3}0.859{col 62}{space 4} -14.8885{col 75}{space 3} 12.41406
{txt}{space 16}212  {c |}{col 22}{res}{space 2}-2.481796{col 34}{space 2} 8.454539{col 45}{space 1}   -0.29{col 54}{space 3}0.769{col 62}{space 4}-19.05239{col 75}{space 3}  14.0888
{txt}{space 16}220  {c |}{col 22}{res}{space 2} .8885889{col 34}{space 2} .7391633{col 45}{space 1}    1.20{col 54}{space 3}0.229{col 62}{space 4}-.5601447{col 75}{space 3} 2.337322
{txt}{space 16}230  {c |}{col 22}{res}{space 2} .9316541{col 34}{space 2} 2.491493{col 45}{space 1}    0.37{col 54}{space 3}0.708{col 62}{space 4}-3.951583{col 75}{space 3} 5.814891
{txt}{space 16}235  {c |}{col 22}{res}{space 2}-.4848092{col 34}{space 2} 7.052819{col 45}{space 1}   -0.07{col 54}{space 3}0.945{col 62}{space 4}-14.30808{col 75}{space 3} 13.33846
{txt}{space 16}255  {c |}{col 22}{res}{space 2} .7199191{col 34}{space 2} 2.389412{col 45}{space 1}    0.30{col 54}{space 3}0.763{col 62}{space 4}-3.963243{col 75}{space 3} 5.403081
{txt}{space 16}290  {c |}{col 22}{res}{space 2}-.9086836{col 34}{space 2} 3.509251{col 45}{space 1}   -0.26{col 54}{space 3}0.796{col 62}{space 4}-7.786689{col 75}{space 3} 5.969322
{txt}{space 16}305  {c |}{col 22}{res}{space 2} 1.269101{col 34}{space 2} 7.542104{col 45}{space 1}    0.17{col 54}{space 3}0.866{col 62}{space 4}-13.51315{col 75}{space 3} 16.05135
{txt}{space 16}310  {c |}{col 22}{res}{space 2} .1667495{col 34}{space 2} 7.320337{col 45}{space 1}    0.02{col 54}{space 3}0.982{col 62}{space 4}-14.18085{col 75}{space 3} 14.51435
{txt}{space 16}316  {c |}{col 22}{res}{space 2}-.5465772{col 34}{space 2} 6.935655{col 45}{space 1}   -0.08{col 54}{space 3}0.937{col 62}{space 4}-14.14021{col 75}{space 3} 13.04706
{txt}{space 16}317  {c |}{col 22}{res}{space 2}-1.013296{col 34}{space 2} 7.868451{col 45}{space 1}   -0.13{col 54}{space 3}0.898{col 62}{space 4}-16.43518{col 75}{space 3} 14.40859
{txt}{space 16}325  {c |}{col 22}{res}{space 2} .4565548{col 34}{space 2} .7357944{col 45}{space 1}    0.62{col 54}{space 3}0.535{col 62}{space 4}-.9855757{col 75}{space 3} 1.898685
{txt}{space 16}338  {c |}{col 22}{res}{space 2}-1.862712{col 34}{space 2} 8.464707{col 45}{space 1}   -0.22{col 54}{space 3}0.826{col 62}{space 4}-18.45323{col 75}{space 3} 14.72781
{txt}{space 16}344  {c |}{col 22}{res}{space 2} .8761955{col 34}{space 2} 7.942966{col 45}{space 1}    0.11{col 54}{space 3}0.912{col 62}{space 4}-14.69173{col 75}{space 3} 16.44412
{txt}{space 16}349  {c |}{col 22}{res}{space 2}-1.614287{col 34}{space 2} 8.174189{col 45}{space 1}   -0.20{col 54}{space 3}0.843{col 62}{space 4} -17.6354{col 75}{space 3} 14.40683
{txt}{space 16}350  {c |}{col 22}{res}{space 2} .3936574{col 34}{space 2}  7.11465{col 45}{space 1}    0.06{col 54}{space 3}0.956{col 62}{space 4} -13.5508{col 75}{space 3} 14.33812
{txt}{space 16}352  {c |}{col 22}{res}{space 2}-.7151695{col 34}{space 2}  8.20917{col 45}{space 1}   -0.09{col 54}{space 3}0.931{col 62}{space 4}-16.80485{col 75}{space 3} 15.37451
{txt}{space 16}355  {c |}{col 22}{res}{space 2}-.6260202{col 34}{space 2} 7.536775{col 45}{space 1}   -0.08{col 54}{space 3}0.934{col 62}{space 4}-15.39783{col 75}{space 3} 14.14579
{txt}{space 16}360  {c |}{col 22}{res}{space 2}-1.026989{col 34}{space 2} 5.733774{col 45}{space 1}   -0.18{col 54}{space 3}0.858{col 62}{space 4}-12.26498{col 75}{space 3}   10.211
{txt}{space 16}366  {c |}{col 22}{res}{space 2}-1.998801{col 34}{space 2} 8.322301{col 45}{space 1}   -0.24{col 54}{space 3}0.810{col 62}{space 4}-18.31021{col 75}{space 3} 14.31261
{txt}{space 16}367  {c |}{col 22}{res}{space 2} .0131817{col 34}{space 2} 8.294272{col 45}{space 1}    0.00{col 54}{space 3}0.999{col 62}{space 4}-16.24329{col 75}{space 3} 16.26966
{txt}{space 16}368  {c |}{col 22}{res}{space 2}-1.411923{col 34}{space 2} 8.239283{col 45}{space 1}   -0.17{col 54}{space 3}0.864{col 62}{space 4}-17.56062{col 75}{space 3} 14.73678
{txt}{space 16}375  {c |}{col 22}{res}{space 2} .6026468{col 34}{space 2} 7.760337{col 45}{space 1}    0.08{col 54}{space 3}0.938{col 62}{space 4}-14.60733{col 75}{space 3} 15.81263
{txt}{space 16}380  {c |}{col 22}{res}{space 2} .4365577{col 34}{space 2} 7.368697{col 45}{space 1}    0.06{col 54}{space 3}0.953{col 62}{space 4}-14.00582{col 75}{space 3} 14.87894
{txt}{space 16}390  {c |}{col 22}{res}{space 2} .3137715{col 34}{space 2} 7.691503{col 45}{space 1}    0.04{col 54}{space 3}0.967{col 62}{space 4} -14.7613{col 75}{space 3} 15.38884
{txt}{space 20} {c |}
{space 15}party {c |}
{space 16}EPP  {c |}{col 22}{res}{space 2} 2.170952{col 34}{space 2}  .963752{col 45}{space 1}    2.25{col 54}{space 3}0.024{col 62}{space 4} .2820327{col 75}{space 3} 4.059871
{txt}{space 16}S&D  {c |}{col 22}{res}{space 2} 2.161944{col 34}{space 2} .9855054{col 45}{space 1}    2.19{col 54}{space 3}0.028{col 62}{space 4}  .230389{col 75}{space 3} 4.093499
{txt}{space 9}Greens/EFA  {c |}{col 22}{res}{space 2}-.8350989{col 34}{space 2} .9867464{col 45}{space 1}   -0.85{col 54}{space 3}0.397{col 62}{space 4}-2.769086{col 75}{space 3} 1.098889
{txt}{space 10}ALDE/ADLE  {c |}{col 22}{res}{space 2} 2.338513{col 34}{space 2} 1.011138{col 45}{space 1}    2.31{col 54}{space 3}0.021{col 62}{space 4} .3567192{col 75}{space 3} 4.320306
{txt}{space 16}ECR  {c |}{col 22}{res}{space 2}  4.00048{col 34}{space 2} 1.001011{col 45}{space 1}    4.00{col 54}{space 3}0.000{col 62}{space 4} 2.038534{col 75}{space 3} 5.962427
{txt}{space 15}EFDD  {c |}{col 22}{res}{space 2}  .104152{col 34}{space 2} .9348446{col 45}{space 1}    0.11{col 54}{space 3}0.911{col 62}{space 4} -1.72811{col 75}{space 3} 1.936414
{txt}{space 12}GUE-NGL  {c |}{col 22}{res}{space 2}-.9562624{col 34}{space 2} 1.260315{col 45}{space 1}   -0.76{col 54}{space 3}0.448{col 62}{space 4}-3.426435{col 75}{space 3}  1.51391
{txt}{space 20} {c |}
{space 15}_cons {c |}{col 22}{res}{space 2}-1.231982{col 34}{space 2} 8.985589{col 45}{space 1}   -0.14{col 54}{space 3}0.891{col 62}{space 4}-18.84341{col 75}{space 3} 16.37945
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELI4, title(Model I4)
{txt}
{com}. //Model I5
. logit prosanctions c.migrantshare##c.immigrantpolicy population unemployment gdpgrowth distance col libyabill iranbill i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-647.98435}  
Iteration 2:{space 3}log pseudolikelihood = {res:-641.20901}  
Iteration 3:{space 3}log pseudolikelihood = {res: -640.8552}  
Iteration 4:{space 3}log pseudolikelihood = {res:-640.85147}  
Iteration 5:{space 3}log pseudolikelihood = {res:-640.85147}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}Wald chi2({res}17{txt}){col 67}= {res}    954.78
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-640.85147{txt}{col 49}Pseudo R2{col 67}= {res}    0.4533

{txt}{ralign 98:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 33}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 34}{c |}{col 46}    Robust
{col 1}                    prosanctions{col 34}{c |}      Coef.{col 46}   Std. Err.{col 58}      z{col 66}   P>|z|{col 74}     [95% Con{col 87}f. Interval]
{hline 33}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}migrantshare {c |}{col 34}{res}{space 2}-30.51654{col 46}{space 2} 13.47725{col 57}{space 1}   -2.26{col 66}{space 3}0.024{col 74}{space 4}-56.93147{col 87}{space 3}-4.101607
{txt}{space 17}immigrantpolicy {c |}{col 34}{res}{space 2} -.571762{col 46}{space 2} .3018471{col 57}{space 1}   -1.89{col 66}{space 3}0.058{col 74}{space 4}-1.163371{col 87}{space 3} .0198473
{txt}{space 32} {c |}
c.migrantshare#c.immigrantpolicy {c |}{col 34}{res}{space 2} 11.78502{col 46}{space 2} 5.744577{col 57}{space 1}    2.05{col 66}{space 3}0.040{col 74}{space 4} .5258541{col 87}{space 3} 23.04418
{txt}{space 32} {c |}
{space 22}population {c |}{col 34}{res}{space 2}-.0071975{col 46}{space 2} .0032858{col 57}{space 1}   -2.19{col 66}{space 3}0.028{col 74}{space 4}-.0136375{col 87}{space 3}-.0007575
{txt}{space 20}unemployment {c |}{col 34}{res}{space 2}-.0371184{col 46}{space 2} .0200471{col 57}{space 1}   -1.85{col 66}{space 3}0.064{col 74}{space 4}-.0764099{col 87}{space 3} .0021731
{txt}{space 23}gdpgrowth {c |}{col 34}{res}{space 2}  .054031{col 46}{space 2} .0632574{col 57}{space 1}    0.85{col 66}{space 3}0.393{col 74}{space 4}-.0699513{col 87}{space 3} .1780133
{txt}{space 24}distance {c |}{col 34}{res}{space 2}  .000253{col 46}{space 2} .0001095{col 57}{space 1}    2.31{col 66}{space 3}0.021{col 74}{space 4} .0000384{col 87}{space 3} .0004676
{txt}{space 29}col {c |}{col 34}{res}{space 2} .0347424{col 46}{space 2} .1839496{col 57}{space 1}    0.19{col 66}{space 3}0.850{col 74}{space 4}-.3257921{col 87}{space 3} .3952769
{txt}{space 23}libyabill {c |}{col 34}{res}{space 2}  4.02682{col 46}{space 2} .3195709{col 57}{space 1}   12.60{col 66}{space 3}0.000{col 74}{space 4} 3.400473{col 87}{space 3} 4.653167
{txt}{space 24}iranbill {c |}{col 34}{res}{space 2} 3.757405{col 46}{space 2} .3780625{col 57}{space 1}    9.94{col 66}{space 3}0.000{col 74}{space 4} 3.016417{col 87}{space 3} 4.498394
{txt}{space 32} {c |}
{space 27}party {c |}
{space 28}EPP  {c |}{col 34}{res}{space 2} 2.340792{col 46}{space 2} .9085437{col 57}{space 1}    2.58{col 66}{space 3}0.010{col 74}{space 4} .5600788{col 87}{space 3} 4.121505
{txt}{space 28}S&D  {c |}{col 34}{res}{space 2} 2.339578{col 46}{space 2} .9225589{col 57}{space 1}    2.54{col 66}{space 3}0.011{col 74}{space 4} .5313953{col 87}{space 3}  4.14776
{txt}{space 21}Greens/EFA  {c |}{col 34}{res}{space 2}-.6954515{col 46}{space 2} .9028506{col 57}{space 1}   -0.77{col 66}{space 3}0.441{col 74}{space 4}-2.465006{col 87}{space 3} 1.074103
{txt}{space 22}ALDE/ADLE  {c |}{col 34}{res}{space 2} 2.499956{col 46}{space 2}  .932251{col 57}{space 1}    2.68{col 66}{space 3}0.007{col 74}{space 4} .6727776{col 87}{space 3} 4.327134
{txt}{space 28}ECR  {c |}{col 34}{res}{space 2} 4.170226{col 46}{space 2} 1.000066{col 57}{space 1}    4.17{col 66}{space 3}0.000{col 74}{space 4} 2.210132{col 87}{space 3}  6.13032
{txt}{space 27}EFDD  {c |}{col 34}{res}{space 2} .3249253{col 46}{space 2} .9029281{col 57}{space 1}    0.36{col 66}{space 3}0.719{col 74}{space 4}-1.444781{col 87}{space 3} 2.094632
{txt}{space 24}GUE-NGL  {c |}{col 34}{res}{space 2}-.6970338{col 46}{space 2} 1.109161{col 57}{space 1}   -0.63{col 66}{space 3}0.530{col 74}{space 4}-2.870949{col 87}{space 3} 1.476882
{txt}{space 32} {c |}
{space 27}_cons {c |}{col 34}{res}{space 2}-2.764752{col 46}{space 2}  1.14456{col 57}{space 1}   -2.42{col 66}{space 3}0.016{col 74}{space 4} -5.00805{col 87}{space 3}-.5214554
{txt}{hline 33}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELI5, title(Model I5)
{txt}
{com}. //Model I6
. logit prosanctions migrantshare sstax migrantshareXsstax population unemployment gdpgrowth distance col libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1045.4561}  
Iteration 1:{space 3}log pseudolikelihood = {res:-590.61174}  
Iteration 2:{space 3}log pseudolikelihood = {res: -583.7614}  
Iteration 3:{space 3}log pseudolikelihood = {res: -583.3961}  
Iteration 4:{space 3}log pseudolikelihood = {res:-583.39216}  
Iteration 5:{space 3}log pseudolikelihood = {res:-583.39215}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,522
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(16)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-583.39215{txt}{col 49}Pseudo R2{col 67}= {res}    0.4420

{txt}{ralign 84:(Std. Err. adjusted for {res:22} clusters in ccode)}
{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}      prosanctions{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}migrantshare {c |}{col 20}{res}{space 2}-7.517752{col 32}{space 2} 3.111988{col 43}{space 1}   -2.42{col 52}{space 3}0.016{col 60}{space 4}-13.61714{col 73}{space 3}-1.418368
{txt}{space 13}sstax {c |}{col 20}{res}{space 2} .2353415{col 32}{space 2} .5350431{col 43}{space 1}    0.44{col 52}{space 3}0.660{col 60}{space 4}-.8133237{col 73}{space 3} 1.284007
{txt}migrantshareXsstax {c |}{col 20}{res}{space 2}  .104407{col 32}{space 2} .1855966{col 43}{space 1}    0.56{col 52}{space 3}0.574{col 60}{space 4}-.2593558{col 73}{space 3} .4681697
{txt}{space 8}population {c |}{col 20}{res}{space 2}  .076494{col 32}{space 2} .1394628{col 43}{space 1}    0.55{col 52}{space 3}0.583{col 60}{space 4} -.196848{col 73}{space 3}  .349836
{txt}{space 6}unemployment {c |}{col 20}{res}{space 2}-.0676733{col 32}{space 2} .0536525{col 43}{space 1}   -1.26{col 52}{space 3}0.207{col 60}{space 4}-.1728303{col 73}{space 3} .0374837
{txt}{space 9}gdpgrowth {c |}{col 20}{res}{space 2}  .055412{col 32}{space 2} .0997323{col 43}{space 1}    0.56{col 52}{space 3}0.578{col 60}{space 4}-.1400598{col 73}{space 3} .2508837
{txt}{space 10}distance {c |}{col 20}{res}{space 2}-.0004379{col 32}{space 2} .0002004{col 43}{space 1}   -2.18{col 52}{space 3}0.029{col 60}{space 4}-.0008308{col 73}{space 3}-.0000451
{txt}{space 15}col {c |}{col 20}{res}{space 2}  .666829{col 32}{space 2} .3797086{col 43}{space 1}    1.76{col 52}{space 3}0.079{col 60}{space 4}-.0773862{col 73}{space 3} 1.411044
{txt}{space 9}libyabill {c |}{col 20}{res}{space 2} 3.181825{col 32}{space 2} .3333585{col 43}{space 1}    9.54{col 52}{space 3}0.000{col 60}{space 4} 2.528455{col 73}{space 3} 3.835196
{txt}{space 10}iranbill {c |}{col 20}{res}{space 2} 4.707109{col 32}{space 2} .5112167{col 43}{space 1}    9.21{col 52}{space 3}0.000{col 60}{space 4} 3.705142{col 73}{space 3} 5.709075
{txt}{space 18} {c |}
{space 13}ccode {c |}
{space 14}205  {c |}{col 20}{res}{space 2} 4.128283{col 32}{space 2} 8.327228{col 43}{space 1}    0.50{col 52}{space 3}0.620{col 60}{space 4}-12.19278{col 73}{space 3} 20.44935
{txt}{space 14}210  {c |}{col 20}{res}{space 2} 2.549226{col 32}{space 2} 8.151476{col 43}{space 1}    0.31{col 52}{space 3}0.754{col 60}{space 4}-13.42737{col 73}{space 3} 18.52582
{txt}{space 14}211  {c |}{col 20}{res}{space 2}  1.45802{col 32}{space 2} 8.932931{col 43}{space 1}    0.16{col 52}{space 3}0.870{col 60}{space 4} -16.0502{col 73}{space 3} 18.96624
{txt}{space 14}212  {c |}{col 20}{res}{space 2} 2.915972{col 32}{space 2} 9.610364{col 43}{space 1}    0.30{col 52}{space 3}0.762{col 60}{space 4}   -15.92{col 73}{space 3} 21.75194
{txt}{space 14}220  {c |}{col 20}{res}{space 2}-4.071299{col 32}{space 2}   6.4114{col 43}{space 1}   -0.64{col 52}{space 3}0.525{col 60}{space 4}-16.63741{col 73}{space 3} 8.494814
{txt}{space 14}230  {c |}{col 20}{res}{space 2} .5401659{col 32}{space 2} 4.235755{col 43}{space 1}    0.13{col 52}{space 3}0.899{col 60}{space 4}-7.761761{col 73}{space 3} 8.842093
{txt}{space 14}235  {c |}{col 20}{res}{space 2} 4.164628{col 32}{space 2} 7.775872{col 43}{space 1}    0.54{col 52}{space 3}0.592{col 60}{space 4} -11.0758{col 73}{space 3} 19.40506
{txt}{space 14}255  {c |}{col 20}{res}{space 2}-3.572995{col 32}{space 2} 4.574845{col 43}{space 1}   -0.78{col 52}{space 3}0.435{col 60}{space 4}-12.53953{col 73}{space 3} 5.393536
{txt}{space 14}290  {c |}{col 20}{res}{space 2}-.1241593{col 32}{space 2} 5.098989{col 43}{space 1}   -0.02{col 52}{space 3}0.981{col 60}{space 4}-10.11799{col 73}{space 3} 9.869675
{txt}{space 14}305  {c |}{col 20}{res}{space 2} .9025868{col 32}{space 2} 10.18699{col 43}{space 1}    0.09{col 52}{space 3}0.929{col 60}{space 4}-19.06355{col 73}{space 3} 20.86872
{txt}{space 14}310  {c |}{col 20}{res}{space 2} 1.711721{col 32}{space 2} 8.732111{col 43}{space 1}    0.20{col 52}{space 3}0.845{col 60}{space 4} -15.4029{col 73}{space 3} 18.82634
{txt}{space 14}316  {c |}{col 20}{res}{space 2}  1.79999{col 32}{space 2} 9.085535{col 43}{space 1}    0.20{col 52}{space 3}0.843{col 60}{space 4}-16.00733{col 73}{space 3} 19.60731
{txt}{space 14}317  {c |}{col 20}{res}{space 2} 2.129889{col 32}{space 2} 9.518324{col 43}{space 1}    0.22{col 52}{space 3}0.823{col 60}{space 4}-16.52568{col 73}{space 3} 20.78546
{txt}{space 14}325  {c |}{col 20}{res}{space 2}-2.431209{col 32}{space 2} 3.837366{col 43}{space 1}   -0.63{col 52}{space 3}0.526{col 60}{space 4}-9.952307{col 73}{space 3}  5.08989
{txt}{space 14}349  {c |}{col 20}{res}{space 2} 1.908637{col 32}{space 2} 10.26379{col 43}{space 1}    0.19{col 52}{space 3}0.852{col 60}{space 4}-18.20802{col 73}{space 3} 22.02529
{txt}{space 14}350  {c |}{col 20}{res}{space 2} 2.019343{col 32}{space 2} 8.219506{col 43}{space 1}    0.25{col 52}{space 3}0.806{col 60}{space 4}-14.09059{col 73}{space 3} 18.12928
{txt}{space 14}366  {c |}{col 20}{res}{space 2}  3.00635{col 32}{space 2}  9.66438{col 43}{space 1}    0.31{col 52}{space 3}0.756{col 60}{space 4}-15.93549{col 73}{space 3} 21.94819
{txt}{space 14}367  {c |}{col 20}{res}{space 2} 4.018267{col 32}{space 2}  8.93869{col 43}{space 1}    0.45{col 52}{space 3}0.653{col 60}{space 4}-13.50124{col 73}{space 3} 21.53778
{txt}{space 14}375  {c |}{col 20}{res}{space 2} 3.754565{col 32}{space 2} 9.178528{col 43}{space 1}    0.41{col 52}{space 3}0.682{col 60}{space 4}-14.23502{col 73}{space 3} 21.74415
{txt}{space 14}380  {c |}{col 20}{res}{space 2} 3.359571{col 32}{space 2} 9.452932{col 43}{space 1}    0.36{col 52}{space 3}0.722{col 60}{space 4}-15.16784{col 73}{space 3} 21.88698
{txt}{space 14}390  {c |}{col 20}{res}{space 2} 6.020049{col 32}{space 2} 8.336071{col 43}{space 1}    0.72{col 52}{space 3}0.470{col 60}{space 4}-10.31835{col 73}{space 3} 22.35845
{txt}{space 18} {c |}
{space 13}party {c |}
{space 14}EPP  {c |}{col 20}{res}{space 2} 2.271407{col 32}{space 2} .9999354{col 43}{space 1}    2.27{col 52}{space 3}0.023{col 60}{space 4} .3115696{col 73}{space 3} 4.231244
{txt}{space 14}S&D  {c |}{col 20}{res}{space 2} 2.229527{col 32}{space 2} 1.013787{col 43}{space 1}    2.20{col 52}{space 3}0.028{col 60}{space 4} .2425399{col 73}{space 3} 4.216514
{txt}{space 7}Greens/EFA  {c |}{col 20}{res}{space 2}-.6997269{col 32}{space 2} 1.005674{col 43}{space 1}   -0.70{col 52}{space 3}0.487{col 60}{space 4}-2.670812{col 73}{space 3} 1.271358
{txt}{space 8}ALDE/ADLE  {c |}{col 20}{res}{space 2} 2.456164{col 32}{space 2} 1.060525{col 43}{space 1}    2.32{col 52}{space 3}0.021{col 60}{space 4} .3775741{col 73}{space 3} 4.534755
{txt}{space 14}ECR  {c |}{col 20}{res}{space 2} 4.188717{col 32}{space 2} 1.022598{col 43}{space 1}    4.10{col 52}{space 3}0.000{col 60}{space 4} 2.184461{col 73}{space 3} 6.192973
{txt}{space 13}EFDD  {c |}{col 20}{res}{space 2} .0856841{col 32}{space 2} .9305876{col 43}{space 1}    0.09{col 52}{space 3}0.927{col 60}{space 4}-1.738234{col 73}{space 3} 1.909602
{txt}{space 10}GUE-NGL  {c |}{col 20}{res}{space 2} -.858188{col 32}{space 2} 1.286771{col 43}{space 1}   -0.67{col 52}{space 3}0.505{col 60}{space 4}-3.380213{col 73}{space 3} 1.663837
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2}-7.876696{col 32}{space 2}  9.23919{col 43}{space 1}   -0.85{col 52}{space 3}0.394{col 60}{space 4}-25.98518{col 73}{space 3} 10.23178
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELI6, title(Model I6)
{txt}
{com}. 
. **TABLE A13
. //Model I7
. logit prosanctions migrantshare2 RWPvoteshare migrantshare2Xrwpvote population unemployment gdpgrowth distance col libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-629.35068}  
Iteration 2:{space 3}log pseudolikelihood = {res:-619.28147}  
Iteration 3:{space 3}log pseudolikelihood = {res:-618.77856}  
Iteration 4:{space 3}log pseudolikelihood = {res:-618.77318}  
Iteration 5:{space 3}log pseudolikelihood = {res:-618.77318}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(16)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-618.77318{txt}{col 49}Pseudo R2{col 67}= {res}    0.4722

{txt}{ralign 87:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}         prosanctions{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}migrantshare2 {c |}{col 23}{res}{space 2}-.6243764{col 35}{space 2} .2297537{col 46}{space 1}   -2.72{col 55}{space 3}0.007{col 63}{space 4}-1.074685{col 76}{space 3}-.1740675
{txt}{space 9}RWPvoteshare {c |}{col 23}{res}{space 2}-.0855187{col 35}{space 2} .0494104{col 46}{space 1}   -1.73{col 55}{space 3}0.083{col 63}{space 4}-.1823614{col 76}{space 3} .0113239
{txt}migrantshare2Xrwpvote {c |}{col 23}{res}{space 2}-.0203054{col 35}{space 2} .0293788{col 46}{space 1}   -0.69{col 55}{space 3}0.489{col 63}{space 4}-.0778868{col 76}{space 3}  .037276
{txt}{space 11}population {c |}{col 23}{res}{space 2} .0305357{col 35}{space 2} .1210959{col 46}{space 1}    0.25{col 55}{space 3}0.801{col 63}{space 4}-.2068079{col 76}{space 3} .2678793
{txt}{space 9}unemployment {c |}{col 23}{res}{space 2}-.1025263{col 35}{space 2} .0388399{col 46}{space 1}   -2.64{col 55}{space 3}0.008{col 63}{space 4}-.1786511{col 76}{space 3}-.0264015
{txt}{space 12}gdpgrowth {c |}{col 23}{res}{space 2} .0543222{col 35}{space 2} .0755876{col 46}{space 1}    0.72{col 55}{space 3}0.472{col 63}{space 4}-.0938268{col 76}{space 3} .2024712
{txt}{space 13}distance {c |}{col 23}{res}{space 2}-.0002536{col 35}{space 2} .0002205{col 46}{space 1}   -1.15{col 55}{space 3}0.250{col 63}{space 4}-.0006859{col 76}{space 3} .0001786
{txt}{space 18}col {c |}{col 23}{res}{space 2} .1998305{col 35}{space 2} .2908392{col 46}{space 1}    0.69{col 55}{space 3}0.492{col 63}{space 4}-.3702039{col 76}{space 3} .7698649
{txt}{space 12}libyabill {c |}{col 23}{res}{space 2} 3.721222{col 35}{space 2} .4341423{col 46}{space 1}    8.57{col 55}{space 3}0.000{col 63}{space 4} 2.870319{col 76}{space 3} 4.572125
{txt}{space 13}iranbill {c |}{col 23}{res}{space 2} 4.748314{col 35}{space 2} .4731706{col 46}{space 1}   10.04{col 55}{space 3}0.000{col 63}{space 4} 3.820917{col 76}{space 3} 5.675711
{txt}{space 21} {c |}
{space 16}ccode {c |}
{space 17}205  {c |}{col 23}{res}{space 2} 1.281007{col 35}{space 2} 7.197202{col 46}{space 1}    0.18{col 55}{space 3}0.859{col 63}{space 4}-12.82525{col 76}{space 3} 15.38726
{txt}{space 17}210  {c |}{col 23}{res}{space 2} 3.608752{col 35}{space 2} 5.773475{col 46}{space 1}    0.63{col 55}{space 3}0.532{col 63}{space 4}-7.707051{col 76}{space 3} 14.92455
{txt}{space 17}211  {c |}{col 23}{res}{space 2} 1.649371{col 35}{space 2}  6.36932{col 46}{space 1}    0.26{col 55}{space 3}0.796{col 63}{space 4}-10.83427{col 76}{space 3} 14.13301
{txt}{space 17}212  {c |}{col 23}{res}{space 2} 1.270757{col 35}{space 2} 7.709091{col 46}{space 1}    0.16{col 55}{space 3}0.869{col 63}{space 4}-13.83878{col 76}{space 3}  16.3803
{txt}{space 17}220  {c |}{col 23}{res}{space 2} .2451058{col 35}{space 2} .7197583{col 46}{space 1}    0.34{col 55}{space 3}0.733{col 63}{space 4}-1.165594{col 76}{space 3} 1.655806
{txt}{space 17}230  {c |}{col 23}{res}{space 2} 1.724345{col 35}{space 2} 2.300843{col 46}{space 1}    0.75{col 55}{space 3}0.454{col 63}{space 4}-2.785225{col 76}{space 3} 6.233914
{txt}{space 17}235  {c |}{col 23}{res}{space 2} 2.622917{col 35}{space 2} 6.396348{col 46}{space 1}    0.41{col 55}{space 3}0.682{col 63}{space 4}-9.913694{col 76}{space 3} 15.15953
{txt}{space 17}255  {c |}{col 23}{res}{space 2}-.5399265{col 35}{space 2} 2.150906{col 46}{space 1}   -0.25{col 55}{space 3}0.802{col 63}{space 4}-4.755625{col 76}{space 3} 3.675772
{txt}{space 17}290  {c |}{col 23}{res}{space 2} .4362814{col 35}{space 2} 3.235668{col 46}{space 1}    0.13{col 55}{space 3}0.893{col 63}{space 4}-5.905511{col 76}{space 3} 6.778074
{txt}{space 17}305  {c |}{col 23}{res}{space 2} 3.430029{col 35}{space 2} 6.933088{col 46}{space 1}    0.49{col 55}{space 3}0.621{col 63}{space 4}-10.15857{col 76}{space 3} 17.01863
{txt}{space 17}310  {c |}{col 23}{res}{space 2} 2.890635{col 35}{space 2} 6.757985{col 46}{space 1}    0.43{col 55}{space 3}0.669{col 63}{space 4}-10.35477{col 76}{space 3} 16.13604
{txt}{space 17}316  {c |}{col 23}{res}{space 2} 2.257807{col 35}{space 2} 6.357343{col 46}{space 1}    0.36{col 55}{space 3}0.722{col 63}{space 4}-10.20236{col 76}{space 3} 14.71797
{txt}{space 17}317  {c |}{col 23}{res}{space 2}  2.17138{col 35}{space 2} 7.246286{col 46}{space 1}    0.30{col 55}{space 3}0.764{col 63}{space 4}-12.03108{col 76}{space 3} 16.37384
{txt}{space 17}325  {c |}{col 23}{res}{space 2} .1490459{col 35}{space 2} .7595798{col 46}{space 1}    0.20{col 55}{space 3}0.844{col 63}{space 4}-1.339703{col 76}{space 3} 1.637795
{txt}{space 17}338  {c |}{col 23}{res}{space 2} 2.434349{col 35}{space 2}  7.87866{col 46}{space 1}    0.31{col 55}{space 3}0.757{col 63}{space 4}-13.00754{col 76}{space 3} 17.87624
{txt}{space 17}344  {c |}{col 23}{res}{space 2} 3.934295{col 35}{space 2} 7.317858{col 46}{space 1}    0.54{col 55}{space 3}0.591{col 63}{space 4}-10.40844{col 76}{space 3} 18.27703
{txt}{space 17}349  {c |}{col 23}{res}{space 2} 1.720079{col 35}{space 2} 7.502382{col 46}{space 1}    0.23{col 55}{space 3}0.819{col 63}{space 4}-12.98432{col 76}{space 3} 16.42448
{txt}{space 17}350  {c |}{col 23}{res}{space 2} 2.701553{col 35}{space 2} 6.600996{col 46}{space 1}    0.41{col 55}{space 3}0.682{col 63}{space 4}-10.23616{col 76}{space 3} 15.63927
{txt}{space 17}352  {c |}{col 23}{res}{space 2} 2.399332{col 35}{space 2} 7.568317{col 46}{space 1}    0.32{col 55}{space 3}0.751{col 63}{space 4} -12.4343{col 76}{space 3} 17.23296
{txt}{space 17}355  {c |}{col 23}{res}{space 2}  2.62851{col 35}{space 2} 7.005065{col 46}{space 1}    0.38{col 55}{space 3}0.707{col 63}{space 4}-11.10117{col 76}{space 3} 16.35819
{txt}{space 17}360  {c |}{col 23}{res}{space 2} 3.210943{col 35}{space 2} 5.520592{col 46}{space 1}    0.58{col 55}{space 3}0.561{col 63}{space 4}-7.609219{col 76}{space 3}  14.0311
{txt}{space 17}366  {c |}{col 23}{res}{space 2} 1.675931{col 35}{space 2} 7.598441{col 46}{space 1}    0.22{col 55}{space 3}0.825{col 63}{space 4}-13.21674{col 76}{space 3}  16.5686
{txt}{space 17}367  {c |}{col 23}{res}{space 2} 3.268867{col 35}{space 2} 7.603474{col 46}{space 1}    0.43{col 55}{space 3}0.667{col 63}{space 4}-11.63367{col 76}{space 3}  18.1714
{txt}{space 17}368  {c |}{col 23}{res}{space 2} 2.081156{col 35}{space 2} 7.538722{col 46}{space 1}    0.28{col 55}{space 3}0.783{col 63}{space 4}-12.69447{col 76}{space 3} 16.85678
{txt}{space 17}375  {c |}{col 23}{res}{space 2} 3.912932{col 35}{space 2} 7.105068{col 46}{space 1}    0.55{col 55}{space 3}0.582{col 63}{space 4}-10.01275{col 76}{space 3} 17.83861
{txt}{space 17}380  {c |}{col 23}{res}{space 2} 3.286788{col 35}{space 2} 6.712971{col 46}{space 1}    0.49{col 55}{space 3}0.624{col 63}{space 4}-9.870393{col 76}{space 3} 16.44397
{txt}{space 17}390  {c |}{col 23}{res}{space 2} 3.535489{col 35}{space 2} 7.039496{col 46}{space 1}    0.50{col 55}{space 3}0.616{col 63}{space 4}-10.26167{col 76}{space 3} 17.33265
{txt}{space 21} {c |}
{space 16}party {c |}
{space 17}EPP  {c |}{col 23}{res}{space 2} 2.161106{col 35}{space 2} .9663223{col 46}{space 1}    2.24{col 55}{space 3}0.025{col 63}{space 4} .2671493{col 76}{space 3} 4.055063
{txt}{space 17}S&D  {c |}{col 23}{res}{space 2} 2.138852{col 35}{space 2} .9899703{col 46}{space 1}    2.16{col 55}{space 3}0.031{col 63}{space 4} .1985455{col 76}{space 3} 4.079158
{txt}{space 10}Greens/EFA  {c |}{col 23}{res}{space 2}-.7994999{col 35}{space 2} .9836987{col 46}{space 1}   -0.81{col 55}{space 3}0.416{col 63}{space 4}-2.727514{col 76}{space 3} 1.128514
{txt}{space 11}ALDE/ADLE  {c |}{col 23}{res}{space 2} 2.312566{col 35}{space 2} 1.017642{col 46}{space 1}    2.27{col 55}{space 3}0.023{col 63}{space 4} .3180253{col 76}{space 3} 4.307107
{txt}{space 17}ECR  {c |}{col 23}{res}{space 2} 4.034493{col 35}{space 2} .9972288{col 46}{space 1}    4.05{col 55}{space 3}0.000{col 63}{space 4} 2.079961{col 76}{space 3} 5.989026
{txt}{space 16}EFDD  {c |}{col 23}{res}{space 2} .1173835{col 35}{space 2} .9394857{col 46}{space 1}    0.12{col 55}{space 3}0.901{col 63}{space 4}-1.723975{col 76}{space 3} 1.958742
{txt}{space 13}GUE-NGL  {c |}{col 23}{res}{space 2}-.9432779{col 35}{space 2} 1.259534{col 46}{space 1}   -0.75{col 55}{space 3}0.454{col 63}{space 4}-3.411919{col 76}{space 3} 1.525364
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2}-4.098675{col 35}{space 2} 8.293301{col 46}{space 1}   -0.49{col 55}{space 3}0.621{col 63}{space 4}-20.35325{col 76}{space 3}  12.1559
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELI7, title(Model I7)
{txt}
{com}. //Model I8
. logit prosanctions c.migrantshare2##c.immigrantpolicy population unemployment gdpgrowth distance col libyabill iranbill i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1172.2762}  
Iteration 1:{space 3}log pseudolikelihood = {res:-647.77041}  
Iteration 2:{space 3}log pseudolikelihood = {res:-640.21648}  
Iteration 3:{space 3}log pseudolikelihood = {res:-639.84457}  
Iteration 4:{space 3}log pseudolikelihood = {res:-639.84242}  
Iteration 5:{space 3}log pseudolikelihood = {res:-639.84242}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,713
{txt}{col 49}Wald chi2({res}17{txt}){col 67}= {res}   1349.33
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-639.84242{txt}{col 49}Pseudo R2{col 67}= {res}    0.4542

{txt}{ralign 99:(Std. Err. adjusted for {res:28} clusters in ccode)}
{hline 34}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 35}{c |}{col 47}    Robust
{col 1}                     prosanctions{col 35}{c |}      Coef.{col 47}   Std. Err.{col 59}      z{col 67}   P>|z|{col 75}     [95% Con{col 88}f. Interval]
{hline 34}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}migrantshare2 {c |}{col 35}{res}{space 2}-1.974312{col 47}{space 2} .7942002{col 58}{space 1}   -2.49{col 67}{space 3}0.013{col 75}{space 4}-3.530916{col 88}{space 3}-.4177085
{txt}{space 18}immigrantpolicy {c |}{col 35}{res}{space 2}-.9985931{col 47}{space 2} .5003423{col 58}{space 1}   -2.00{col 67}{space 3}0.046{col 75}{space 4}-1.979246{col 88}{space 3}-.0179403
{txt}{space 33} {c |}
c.migrantshare2#c.immigrantpolicy {c |}{col 35}{res}{space 2}  .690789{col 47}{space 2} .3376614{col 58}{space 1}    2.05{col 67}{space 3}0.041{col 75}{space 4} .0289849{col 88}{space 3} 1.352593
{txt}{space 33} {c |}
{space 23}population {c |}{col 35}{res}{space 2}-.0069942{col 47}{space 2} .0034078{col 58}{space 1}   -2.05{col 67}{space 3}0.040{col 75}{space 4}-.0136733{col 88}{space 3}-.0003151
{txt}{space 21}unemployment {c |}{col 35}{res}{space 2}-.0520428{col 47}{space 2} .0219105{col 58}{space 1}   -2.38{col 67}{space 3}0.018{col 75}{space 4}-.0949866{col 88}{space 3} -.009099
{txt}{space 24}gdpgrowth {c |}{col 35}{res}{space 2} .0122184{col 47}{space 2}  .052363{col 58}{space 1}    0.23{col 67}{space 3}0.815{col 75}{space 4}-.0904113{col 88}{space 3} .1148481
{txt}{space 25}distance {c |}{col 35}{res}{space 2} .0001493{col 47}{space 2} .0001196{col 58}{space 1}    1.25{col 67}{space 3}0.212{col 75}{space 4}-.0000851{col 88}{space 3} .0003837
{txt}{space 30}col {c |}{col 35}{res}{space 2}-.0537441{col 47}{space 2} .2079096{col 58}{space 1}   -0.26{col 67}{space 3}0.796{col 75}{space 4}-.4612394{col 88}{space 3} .3537512
{txt}{space 24}libyabill {c |}{col 35}{res}{space 2} 3.832115{col 47}{space 2} .3301336{col 58}{space 1}   11.61{col 67}{space 3}0.000{col 75}{space 4} 3.185065{col 88}{space 3} 4.479165
{txt}{space 25}iranbill {c |}{col 35}{res}{space 2} 3.748887{col 47}{space 2} .3601892{col 58}{space 1}   10.41{col 67}{space 3}0.000{col 75}{space 4} 3.042929{col 88}{space 3} 4.454845
{txt}{space 33} {c |}
{space 28}party {c |}
{space 29}EPP  {c |}{col 35}{res}{space 2}  2.31948{col 47}{space 2}   .93302{col 58}{space 1}    2.49{col 67}{space 3}0.013{col 75}{space 4} .4907949{col 88}{space 3} 4.148166
{txt}{space 29}S&D  {c |}{col 35}{res}{space 2} 2.345216{col 47}{space 2} .9420549{col 58}{space 1}    2.49{col 67}{space 3}0.013{col 75}{space 4} .4988227{col 88}{space 3}  4.19161
{txt}{space 22}Greens/EFA  {c |}{col 35}{res}{space 2}-.6545952{col 47}{space 2} .9146533{col 58}{space 1}   -0.72{col 67}{space 3}0.474{col 75}{space 4}-2.447283{col 88}{space 3} 1.138092
{txt}{space 23}ALDE/ADLE  {c |}{col 35}{res}{space 2} 2.518005{col 47}{space 2} .9530046{col 58}{space 1}    2.64{col 67}{space 3}0.008{col 75}{space 4} .6501508{col 88}{space 3}  4.38586
{txt}{space 29}ECR  {c |}{col 35}{res}{space 2} 4.251188{col 47}{space 2} 1.027973{col 58}{space 1}    4.14{col 67}{space 3}0.000{col 75}{space 4} 2.236398{col 88}{space 3} 6.265977
{txt}{space 28}EFDD  {c |}{col 35}{res}{space 2} .3076964{col 47}{space 2} .9262433{col 58}{space 1}    0.33{col 67}{space 3}0.740{col 75}{space 4}-1.507707{col 88}{space 3}   2.1231
{txt}{space 25}GUE-NGL  {c |}{col 35}{res}{space 2} -.624524{col 47}{space 2} 1.122967{col 58}{space 1}   -0.56{col 67}{space 3}0.578{col 75}{space 4}-2.825498{col 88}{space 3} 1.576451
{txt}{space 33} {c |}
{space 28}_cons {c |}{col 35}{res}{space 2}-1.060788{col 47}{space 2} 1.829917{col 58}{space 1}   -0.58{col 67}{space 3}0.562{col 75}{space 4}-4.647359{col 88}{space 3} 2.525783
{txt}{hline 34}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELI8, title(Model I8)
{txt}
{com}. //Model I9
. logit prosanctions migrantshare2 sstax migrantshare2Xsstax population unemployment gdpgrowth distance col libyabill iranbill i.ccode i.party, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1045.4561}  
Iteration 1:{space 3}log pseudolikelihood = {res:-590.02948}  
Iteration 2:{space 3}log pseudolikelihood = {res:-583.04889}  
Iteration 3:{space 3}log pseudolikelihood = {res: -582.6767}  
Iteration 4:{space 3}log pseudolikelihood = {res:-582.67394}  
Iteration 5:{space 3}log pseudolikelihood = {res:-582.67394}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,522
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(16)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-582.67394{txt}{col 49}Pseudo R2{col 67}= {res}    0.4427

{txt}{ralign 85:(Std. Err. adjusted for {res:22} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}       prosanctions{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}migrantshare2 {c |}{col 21}{res}{space 2}-.8347804{col 33}{space 2} .2459875{col 44}{space 1}   -3.39{col 53}{space 3}0.001{col 61}{space 4}-1.316907{col 74}{space 3}-.3526538
{txt}{space 14}sstax {c |}{col 21}{res}{space 2} .2532408{col 33}{space 2} .5269682{col 44}{space 1}    0.48{col 53}{space 3}0.631{col 61}{space 4} -.779598{col 74}{space 3}  1.28608
{txt}migrantshare2Xsstax {c |}{col 21}{res}{space 2}-.0050474{col 33}{space 2} .0144149{col 44}{space 1}   -0.35{col 53}{space 3}0.726{col 61}{space 4}   -.0333{col 74}{space 3} .0232052
{txt}{space 9}population {c |}{col 21}{res}{space 2} .0997318{col 33}{space 2} .1402277{col 44}{space 1}    0.71{col 53}{space 3}0.477{col 61}{space 4}-.1751093{col 74}{space 3}  .374573
{txt}{space 7}unemployment {c |}{col 21}{res}{space 2}-.0767001{col 33}{space 2} .0551759{col 44}{space 1}   -1.39{col 53}{space 3}0.164{col 61}{space 4}-.1848429{col 74}{space 3} .0314427
{txt}{space 10}gdpgrowth {c |}{col 21}{res}{space 2} .0498195{col 33}{space 2} .1021925{col 44}{space 1}    0.49{col 53}{space 3}0.626{col 61}{space 4}-.1504741{col 74}{space 3} .2501132
{txt}{space 11}distance {c |}{col 21}{res}{space 2} -.000535{col 33}{space 2} .0002143{col 44}{space 1}   -2.50{col 53}{space 3}0.013{col 61}{space 4}-.0009549{col 74}{space 3} -.000115
{txt}{space 16}col {c |}{col 21}{res}{space 2} .8262998{col 33}{space 2} .3883056{col 44}{space 1}    2.13{col 53}{space 3}0.033{col 61}{space 4} .0652348{col 74}{space 3} 1.587365
{txt}{space 10}libyabill {c |}{col 21}{res}{space 2} 3.069426{col 33}{space 2} .3394584{col 44}{space 1}    9.04{col 53}{space 3}0.000{col 61}{space 4}   2.4041{col 74}{space 3} 3.734752
{txt}{space 11}iranbill {c |}{col 21}{res}{space 2} 4.881273{col 33}{space 2} .5361144{col 44}{space 1}    9.10{col 53}{space 3}0.000{col 61}{space 4} 3.830508{col 74}{space 3} 5.932038
{txt}{space 19} {c |}
{space 14}ccode {c |}
{space 15}205  {c |}{col 21}{res}{space 2}  5.55679{col 33}{space 2} 8.383941{col 44}{space 1}    0.66{col 53}{space 3}0.507{col 61}{space 4}-10.87543{col 74}{space 3} 21.98901
{txt}{space 15}210  {c |}{col 21}{res}{space 2} 3.581903{col 33}{space 2} 8.157831{col 44}{space 1}    0.44{col 53}{space 3}0.661{col 61}{space 4}-12.40715{col 74}{space 3} 19.57096
{txt}{space 15}211  {c |}{col 21}{res}{space 2} 2.512599{col 33}{space 2} 8.937385{col 44}{space 1}    0.28{col 53}{space 3}0.779{col 61}{space 4}-15.00435{col 74}{space 3} 20.02955
{txt}{space 15}212  {c |}{col 21}{res}{space 2} 4.164708{col 33}{space 2} 9.649342{col 44}{space 1}    0.43{col 53}{space 3}0.666{col 61}{space 4}-14.74765{col 74}{space 3} 23.07707
{txt}{space 15}220  {c |}{col 21}{res}{space 2}-4.395683{col 33}{space 2} 6.313244{col 44}{space 1}   -0.70{col 53}{space 3}0.486{col 61}{space 4}-16.76941{col 74}{space 3} 7.978048
{txt}{space 15}230  {c |}{col 21}{res}{space 2} .9258167{col 33}{space 2} 4.204156{col 44}{space 1}    0.22{col 53}{space 3}0.826{col 61}{space 4}-7.314178{col 74}{space 3} 9.165812
{txt}{space 15}235  {c |}{col 21}{res}{space 2} 5.412929{col 33}{space 2}  7.81015{col 44}{space 1}    0.69{col 53}{space 3}0.488{col 61}{space 4}-9.894684{col 74}{space 3} 20.72054
{txt}{space 15}255  {c |}{col 21}{res}{space 2}-4.114122{col 33}{space 2} 4.515536{col 44}{space 1}   -0.91{col 53}{space 3}0.362{col 61}{space 4}-12.96441{col 74}{space 3} 4.736165
{txt}{space 15}290  {c |}{col 21}{res}{space 2} .3201938{col 33}{space 2} 5.095693{col 44}{space 1}    0.06{col 53}{space 3}0.950{col 61}{space 4}-9.667181{col 74}{space 3} 10.30757
{txt}{space 15}305  {c |}{col 21}{res}{space 2} 1.824952{col 33}{space 2} 10.17402{col 44}{space 1}    0.18{col 53}{space 3}0.858{col 61}{space 4}-18.11576{col 74}{space 3} 21.76567
{txt}{space 15}310  {c |}{col 21}{res}{space 2} 2.857112{col 33}{space 2} 8.763092{col 44}{space 1}    0.33{col 53}{space 3}0.744{col 61}{space 4}-14.31823{col 74}{space 3} 20.03246
{txt}{space 15}316  {c |}{col 21}{res}{space 2} 2.817091{col 33}{space 2} 9.078834{col 44}{space 1}    0.31{col 53}{space 3}0.756{col 61}{space 4} -14.9771{col 74}{space 3} 20.61128
{txt}{space 15}317  {c |}{col 21}{res}{space 2} 3.320146{col 33}{space 2} 9.543131{col 44}{space 1}    0.35{col 53}{space 3}0.728{col 61}{space 4}-15.38405{col 74}{space 3} 22.02434
{txt}{space 15}325  {c |}{col 21}{res}{space 2} -2.53595{col 33}{space 2} 3.773493{col 44}{space 1}   -0.67{col 53}{space 3}0.502{col 61}{space 4}-9.931859{col 74}{space 3}  4.85996
{txt}{space 15}349  {c |}{col 21}{res}{space 2} 3.063957{col 33}{space 2} 10.26848{col 44}{space 1}    0.30{col 53}{space 3}0.765{col 61}{space 4}-17.06189{col 74}{space 3} 23.18981
{txt}{space 15}350  {c |}{col 21}{res}{space 2} 3.165251{col 33}{space 2} 8.229093{col 44}{space 1}    0.38{col 53}{space 3}0.701{col 61}{space 4}-12.96347{col 74}{space 3} 19.29398
{txt}{space 15}366  {c |}{col 21}{res}{space 2} 4.361857{col 33}{space 2}  9.70165{col 44}{space 1}    0.45{col 53}{space 3}0.653{col 61}{space 4}-14.65303{col 74}{space 3} 23.37674
{txt}{space 15}367  {c |}{col 21}{res}{space 2} 5.361723{col 33}{space 2} 8.985159{col 44}{space 1}    0.60{col 53}{space 3}0.551{col 61}{space 4}-12.24887{col 74}{space 3} 22.97231
{txt}{space 15}375  {c |}{col 21}{res}{space 2} 5.169621{col 33}{space 2} 9.216892{col 44}{space 1}    0.56{col 53}{space 3}0.575{col 61}{space 4}-12.89516{col 74}{space 3}  23.2344
{txt}{space 15}380  {c |}{col 21}{res}{space 2}  4.47081{col 33}{space 2} 9.430556{col 44}{space 1}    0.47{col 53}{space 3}0.635{col 61}{space 4}-14.01274{col 74}{space 3} 22.95436
{txt}{space 15}390  {c |}{col 21}{res}{space 2}  7.55063{col 33}{space 2} 8.336448{col 44}{space 1}    0.91{col 53}{space 3}0.365{col 61}{space 4}-8.788509{col 74}{space 3} 23.88977
{txt}{space 19} {c |}
{space 14}party {c |}
{space 15}EPP  {c |}{col 21}{res}{space 2} 2.264667{col 33}{space 2} 1.002335{col 44}{space 1}    2.26{col 53}{space 3}0.024{col 61}{space 4} .3001264{col 74}{space 3} 4.229207
{txt}{space 15}S&D  {c |}{col 21}{res}{space 2} 2.220652{col 33}{space 2} 1.016657{col 44}{space 1}    2.18{col 53}{space 3}0.029{col 61}{space 4} .2280398{col 74}{space 3} 4.213264
{txt}{space 8}Greens/EFA  {c |}{col 21}{res}{space 2}-.6931649{col 33}{space 2} 1.005842{col 44}{space 1}   -0.69{col 53}{space 3}0.491{col 61}{space 4}-2.664579{col 74}{space 3} 1.278249
{txt}{space 9}ALDE/ADLE  {c |}{col 21}{res}{space 2} 2.437958{col 33}{space 2} 1.064644{col 44}{space 1}    2.29{col 53}{space 3}0.022{col 61}{space 4} .3512938{col 74}{space 3} 4.524622
{txt}{space 15}ECR  {c |}{col 21}{res}{space 2} 4.179375{col 33}{space 2} 1.024294{col 44}{space 1}    4.08{col 53}{space 3}0.000{col 61}{space 4} 2.171796{col 74}{space 3} 6.186955
{txt}{space 14}EFDD  {c |}{col 21}{res}{space 2} .0851926{col 33}{space 2} .9340869{col 44}{space 1}    0.09{col 53}{space 3}0.927{col 61}{space 4}-1.745584{col 74}{space 3} 1.915969
{txt}{space 11}GUE-NGL  {c |}{col 21}{res}{space 2}-.8727331{col 33}{space 2} 1.293254{col 44}{space 1}   -0.67{col 53}{space 3}0.500{col 61}{space 4}-3.407465{col 74}{space 3} 1.661998
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-9.015497{col 33}{space 2} 9.262719{col 44}{space 1}   -0.97{col 53}{space 3}0.330{col 61}{space 4}-27.17009{col 74}{space 3} 9.139099
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store MODELI9, title(Model I9)
{txt}
{com}. 
{txt}end of do-file

{com}. 
. exit, clear
